Multiplayer Tracking with Diagonal Video to Support Basketball Tactical Learning

18th International Conference on Ubiquitous Information Management and Communication

Player tracking has become a crucial technique for getting strategic advantage in competitive team sports. Movies captured by one or a few cameras are easy to use for this purpose at any level including amateur. This study describes a design issue that integrates up-to-date technologies of multi-object tracking for tactics detection from the actual game data to bring affordability, feasibility, and accessibility.


Dashboard-camera-based Safe Driving Support System

The 8th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science Engineering (BCD 2023)


Basketball Players Identification and Tracking using a Single Fixed Camera

17th International Conference on Signal Image Technology & Internet based Systems

The application of data science in sports has recentlygained significant importance. Consequently, tactics development,opponent team understanding, in-game decision-making, playervital statistics, etc. could all benefit from game data analysis.Given a video input, this study proposes employing consecutiveintersection over union (IOU) based tracking and K-Meansbased uniform colors detection methods to monitor players andidentify teams in basketball. A retrained YOLOv7 algorithmis used to recognize the humans (players, referees, etc.), ball,and field markers. The Kalman filter is employed to tackleocclusion occurrence during tracking. Our findings indicate thatthis technology may be practical and affordable for both amateurand professional teams.


Creation and Testing of Synthetic Datasets for Training Road Scenes Algorithms

32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)


Recognizing football game events: Handball based on Computer Vision


Development of semi-automatic image annotation using object recognition

The modern field of machine learning has made remarkable progress, and the demand for training data required by machine learning systems is constantly increasing. However, the annotation work required to create this training data requiresa great deal of effort. Therefore, we considered semi-automating the labeling process by taking advantage of false positives that occur when object detection is performed by YOLOv5, thereby eliminating the need for region selection and reducing the cost of annotation work. In a comparison experiment with labelImg, a common annotation tool, we found that semi-automating the annotation process took less time than manually annotating the objects.


Image-based Pretreatment Study of Rice Blast Disease

3rd International Conference on Intelligent Systems, Cognitive Science and Knowledge Engineering

Rice blight has a great impact on rice yield and can lead to yield reduction of up to 70% in severe cases. Traditional detection methods require professional technicians to operate and are costly and inefficient, and cannot detect rice diseases in real-time. In this paper, we applied image detection technology to study rice blast disease based on the Matlab platform. Firstly, a basic rice blast database is built, and then a discussion is made on how to effectively improve the recognition success rate of rice blast images by two aspects: image pre-processing and feature extraction. The main research contents are as follows. (1) After studying the existing plant disease database, a basic rice blast database was constructed by field photography and other means. (2) Preprocessing of the collected rice blight images. Using the algorithm of rgb2gray function in Matlab, the images were grayed out; based on this, median filtering was used for noise reduction; then histogram equalization technique was used for image enhancement to increase the contrast and make the images clear; finally, various segmentation algorithms were used for image segmentation. (3) For the pre-processed rice blight images, feature extraction was performed in terms of the color of the disease to pave the way for feature selection.


Pedestrian detection algorithm based on improved YOLOv3

The ability to accurately detect pedestrians in the area of interest in real time is crucial in the field of autonomous driving. An improved YOLOv3 model is proposed for pedestrian detection. Firstly, a lightweight model that incorporates a residual network module approach and a CBAM attention mechanism is added to the structure to enhance the feature representation capability of the network. Experimental results show that the improved YOLOv3 target detection model raises the detection accuracy by 4% compared to the original algorithm, and the accuracy precision is improved to a large extent, which verifies the feasibility and effectiveness of the improved YOLOv3 model for pedestrian detection.


Indoors Fitness Training Monitoring based on OpenPose

With the continuation of the COVID-19 pandemic, people's daily life has changed. The changing life habits are reflected in the increasing number of hours working at home. Mostly affected is physical fitness, because of limitations or fear of the gym/outdoors or effective exercise indoors. However, with the arrival of the post-pandemic era, although working at home has improved, the fitness problem still haunts people. Some people have become accustomed to home fitness and are no longer limited to the traditional gym or gymnasium. However, proper and safe exercising is still a challenge due to the lack of live coaching. With the advent of artificial intelligence and the improvement of virtual reality (VR) and augmented reality (AR) capabilities, the options for live off-site coaching have become feasible. This study is based on OpenPose technology in artificial intelligence to monitor the standard of people's movements in-home fitness. The study results are encouraging.


Vision-based primary localization method for SLAM mobile robots

The AMCL (Adaptive Monte Carlo Localization) algorithm with visual provision of initial values is proposed to address the slow localization speed caused by conventional laser SLAM (Simultaneous Localization and Mapping) without initial poses and the global localization failure after a robot abduction event. In the initial map building phase, the ORB (Oriented FAST and Rotated BRIEF) feature values are extracted from the camera and the wall corners are identified, and then the pose information is stored in the database and a feature dictionary is constructed. After restarting, the dictionary is called to perform loopback detection by receiving the images captured by the current camera, and a successful detection results in a rough initial pose. If the detection fails, the initial pose is roughly calculated by identifying the wall corners. Finally, the particle filtering algorithm scatters particles in a small area near the obtained pose and converges to obtain a relatively accurate pose.


Application of On-Road Surveillance Cameras for Traffic Light Optimization

Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS2022)


Appearance-based Drivers Gaze Mapping Using a Dash Camera


Soccer Players Real Location Determination Using Perspective Transformation


Multi-modal Feature Fusion Network for Breast Lesions Segmentation

The 18th International Conference on Intelligent Unmanned Systems

International Society of Intelligent Unmanned Systems


An intelligent traffic control systems using on-road cameras

SPIE The International Society for Optical Engineering


Continual learning : Linear layer classier concatenation using image processing transform functions


A comparative study of batch ensemble for multi-object tracking approximations in embedded vision


Social distance measurement for indoor environments


Piano performance support system with a head-mounted display

本論文では，VRを用いたピアノ演奏の練習支援手法を提案する．本手法では，演奏者にHMD(ヘッドマウントディスプレイ)を装着してもらい，その画面上に，押すべき鍵盤や指の使い方などを表示して，演奏を導くものである．本手法の有効性を検証するため実験の結果，初心者の演奏スキルを向上させる可能性が確認できた．


Design of a one-on-one training system for basketball players

This paper describes the design of a system to developthe feint skill of basketball players in one-on-one situations.When training alone for one-on-one situations, the player lacksan opponent; therefore such training lacks a realistic aspect.The proposed system uses a software to provide a single traineewith an appropriate opponent. To train an offence player, thesystem monitors and analyzes the body movements of an offensiveplayer, providing a counterpart movement with the silhouette ofa visualized defense, whose functions are based on the analysis ofthe monitored offense movement. To train a defense, the system ismodified to provide a realistic offence using a head mount displayworn by a defense trainee. Defense trainee receives feedback afterthe training.


Facial emotion recognition using deep learning

Proceedings of IEEJ International Workshop on Sensing, Actuation, Motion Control, and Optimization2020

The Institute of Electrical Engineers of Japan


Towards Drone-Video Player Detection and Tracking for Soccer Strategy Analysis

Proceedings of the 6th IEEJ international workshop on Sensing, Actuation, Motion Control, and Optimization


Facial emotion recognition using deep learning ls

Proceeding of the 6th IEEJ international workshop on Sensing, Actuation, Motion Control, and Optimization

Emotion recognition has played an important role in the research field of Human Computer Interactions. Therefore, there is need for wellimplemented automatic emotion recognition systems. Nowadays, Neural Networks are one of the best methods to improve emotionrecognition in this field. There are many emotions in wild data available these days. But, many of them are labeled with noise. In this work,firstly, we trained and compared state of the art models such as Resnet, Seresnet and Densenet in FERPlus dataset. After that, we decide thepre-trained model such as LRCN model for emotion recognition in video.


Risky Pedestrian Detection System using In-vehicle Camera

本稿では車載カメラを用いた危険な歩行者の検出システムを提案する．本手法では，自動車に設置された前方カメラから得られる映像に対して，深層学習を用いることで，歩行者の存在を検出した上で，その人物が走行車に対して危険であるかどうかを判断した上で，ドライバーに警告を与えることが可能である．本手法の有効性を確認するために行なった実験で，高い精度で危険な歩行者のみを検出できることが示唆された．


Tracking Owner of Detected Abandoned Luggage at Public Areas

本論文では，画像処理を用いて放置された手荷物を検出した上で，所有者の追跡する手法を提案する．この手法では公共の場所に設置された監視カメラから得られる映像から学習機能を用いて放置された手荷物を検出し，さらにその所有者を追跡することで，防犯上の問題である公共の場での放置物に対する脅威を取り除くことができる．本手法の有効性を検証する実験で，高い精度で追跡可能であることが示唆された．


Mitigating Constraints in Load Balancing and Scheduling in Embedded Systems with Ensemble Models

本稿では，アンサンブルモデルを用いた負荷分散における制約緩和と組込みシステムにおけるスケジューリング法について提案した．本手法は，複数の予測モデルを組み合わせて最終的な予測結果を出すアンサンブルモデルを用いて，負荷分散における制約の緩和と組込みシステムにおけるスケジューリングを最適化できる．本手法の有効性を確認するためのARMアーキテクチャを用いた実験により，高い性能が実現できることを示した．


Tactics-Trend Analysis for Increasing the Possibility of Shooting in a Basketball Match


Kitchen Utensils Recognition using Fine Tuning and Transfer Learning

Proceedings of International Conference on Video and Image Processing


Player Tracking in Sports Video using 360 Degree Camera

IEEE International Conference on Computational Photography 2019


Human Interaction Recognition Based on Deep Learning

Proceedings of Annual conference on Engineering and Information Technology(ACEAIT2019)

本稿では，深層学習による人のインタラクションの認識手法を提案する．人の動きの認識は動画像処理の分野で注目されているが，近年，深層学習の導入で大きな成果をあげている．本手法では連続フレームにおいて，深層学習を用いることで隠れている時間や空間の問題を解決し，人の動きを認識する．本手法の有効性を検証するためのベンチマークのデータセットを用いた実験により，人の動きを高い精度で認識できることを示した．


Vision-Based Vehicle Recognition Classification Using Convolutional Neural Network and Support Vector Machine

本稿では，改良型のAlexNetを用いた実道路環境下における車両領域抽出と分類手法を提案する．本手法では，車両領域抽出のロバスト性の低さを，高速で軽量化したYOLOを用いて解決し，さらに深層学習とSVMを組み合わせて，SVMで特徴量を正規化することで，モデルの汎化能力を向上させている．本手法の有効性を検証するための実験により，車両検出と車種分類を従来手法よりも高い精度で実行できることを示した．


Predictive Analysis of Cohesiveness in Multivariate Sequences Using Recurrent Neural Networks

The 166th IASTEM International conference


Ground Sports Strategy Formulation and Assistance Technology Development - Player Data Acquisition from Drone Videos

Proceedings of 8th International Conference on Industrial Technology and Management

Sports analytics aims to provide a competitive advantage to a team or individual in sports by analyzing a collection of relevant statistics. Better strategies in play can be formulated using the information. The core technology necessary for the analysis is the player detection and tracking. Due to limitation of fixed cameras, this work presents a court detection using markers, player detection and tracking using a drone. The method presented is based on perspective transformation, template matching and particle filters. In this work, we can visualize the movement trajectory of a player and extract the real world (on court) player position irrespective of drone height and position.


Automatic Recognition of Road Marking using CNN

Proceedings of International Research Conference on Engineering and Technology(IRSET2019)

本稿では，深層学習を用いて，路面標識を自動認識する手法を提案した．本手法では，まず自動車の前方に取り付けられた車載カメラにより連続的に撮像された映像から生成される時空間画像から精度良く道路標示領域を抽出した上で，CNNを用いて道路標示を認識し，その結果を音声ガイドと画面表示で運転者に伝えることができる．本手法の有効性を検証する実験で，精度良く路面標識を自動認識できることが示唆された．


Technology-Supported Single Training for One-on-One in Basketball Matches


Robust Detection and Tracking of Moving Objects Using Particle Filter

Proceedings of Annual Conference on Engineering and Applied Science (ACEAT2018)

本稿では，パーティクルフィルタを用いた移動物体の検出と追跡手法を提案した．本手法では，連続して撮像される映像からHOG特徴とSVMを用いて人の領域を抽出し，パーティクルフィルタを用いて人の動きを追跡することで，人同士が重なる混雑時や照明環境の変動にロバストにすることを可能にしている．本手法の有効性を検証する実験により，環境変動にロバストに移動物体の検出と追跡ができることを示した．


Traffic volume survey Using Convolutional Neural Network

本稿では，道路監視カメラを用いた深層学習による交通流監視法を提案する．本手法は，道路上方に設置された監視カメラから得られる映像より，車両領域を検出した上で，乗用車，軽自動車，小型貨物車，普通貨物車，バス・大型車のいずれであるかを深層学習により認識し，車種別の交通量を計測することができる．本手法の有効性を検証するために行なった実験により，車種別の交通量を高い精度で計測できることを示した．


Proceedings of The 5th Annual Conference on Engineering and Information Technology(2018ACEAIT)

カメラ映像を用いて，カルマンフィルタとサポートベクターマシーンを用いて人の行動監視をする手法を提案した．本手法では画像処理を用いて人を検出し，さらにカルマンフィルタを用いて追跡することで，人の行動を監視できるものである．これにより歩いている人，ジョギングをする人，走っている人を認識することが可能である．本手法の有効性を検証するために実験をしたところ，約70%の精度で人の行動を認識することができることがわかった．


Road Sign Character Recognition On a Smart phone using Deep Learning

4th International Forum on Advanced Technologies

本論文では，スマートフォンで撮影された画像を用いて，道路標識上の文字を認識する手法を提案した．本システムでは，スマートフォンで道路標識を撮影した画像から文字列を抽出し，ディープラーニングを用いて文字認識することができる．さらに文字認識した情報を用いて，GPSで取得した現在位置とGoogleMapを用いて外国人旅行向けのナビゲーションシステムを構築した．本手法の有効性を検証したところ，約83%の精度で文字認識できることがわかった．


Body Physical Parameters Measurement using Kinect

4th International Forum on Advanced Technologie

本論文では，RGB-Dカメラを用いて，人の形状を計測するシステムを提案した．本システムは，Kinectを用いて人の3次元形状を取得した上で，被験者の身長，座高，肩幅，股下，全頭高，膝下，股上，頭身，ウエスト，腕の太さ，わたり幅，首の太さなどの人の各部位のサイズを計測することができる．本手法を用いることで服を仮想的に試着することができる．本手法の有効性を検証したところ，各部位のサイズを誤差5cm以内で計測することがわかった．


Real time backpack detection in visual surveillance based On verticals contour analysis

6th IIAE International Conference on Industrial Application Engineering 2018

本論文では，監視カメラから得られる歩行者が持ち歩くバックパックとスリングバッグを画像処理によって検出する手法を提案した．この手法では，人体の内部または近くに発生する垂直線を見つけた上で，いくつかのファジールールを使用して等高線を作成した上でバックパックとスリングバッグを検出することができる．本手法の有効性を検証するたにVIRAT ground video datasetを用いて実験をしたところ，高い精度を得られることがわかった．


Benchmarking Organizational Performance Dynamics Using Memory Cells Recurrent Neural Networks


Household Items Recognition using Distance Sensor Equipped Video Sunglasses

Risp International Workshop on Nonlinear Circuits, Communications and Signal Processing

The Research Institute of Signal Processing Japan

本稿では，目の不自由な人が台所等で作業するために，家庭用品を認識した上で，手にとりやすいように位置を計測する手法を提案する．この手法では，サングラス型カメラから得られる映像を処理することにより道具の種類を認識して，さらにサングラス型カメラに搭載されている距離センサにより位置を推定することが可能である．本手法の有効性を検証する実験で，目の不自由な方の作業の支援が可能であることが示唆された．


Review of Digital Soil Mapping Procedures

International Journal of Computer Applications proceedings of 2nd ICKE2016


Hyperspectral Remote Sensing for Agriculture: A Review


Biometric Cryptosystem for Finger Print Verification


Feature Tracking using Particle Filer in Rope Skipping for Gross Motor Skill Development


A Learning Support System for Integrated Motor Skill by Organized Training Stages


Observing the Degree of Distortion in Coordinated Motor Actions


Analysis of Rope Skipping Features for Gross Motor Skill Development

International Research Symposium on Engineering and Technology


Body Physical Measurement using Kinect for Virtual Dressing Room

Proceedings of the 6th IIAI International Congress on Advanced Applied Informatics( IIAI-AAI 2017)

本論文では，3DCGを用いた疑似試着システムを提案した．この手法では，RGB-Dカメラを用いて被験者の3Dデータを取得した上で3Dモデルを作成し，さらに予め作成してある服のデータを用いて，仮想空間上で疑似試着をするものである．3Dモデルでは，身長，首の太さ，腕の太さ，ウエストなどの試着に必要なサイズを取得することで，服のフィット感を3DCGで確認できる．実際にKinectを用いた実験によって，本手法の有効性を確認した．


Character Recognition in Road Signs using a Smartphone

本論文では，スマートフォンを用いた道路標識上の文字を認識する手法を提案した．この手法では，スマートフォンで撮影された道路標識に対して，端末上でタップした文字列を認識することができる．本手法では認識にはディープラーニングを用い，さらにGoogle Map APIを適用することで，現在地から目的地までのナビゲーションをすることが可能である．本手法の有効性を検証するために行った実シーンによる実験によって，高精度に文字を認識することがわかった．


Real Time Specified Person Tracking in a Crowded Scene using the Particle Filter

Proceedings of the International Conference on Electrical Engineering (ICEE2017)

本論文では，パーティクルフィルタを用いて，混雑環境下において，人を追跡する手法を提案した．この手法では，まず取得された映像からHOG特徴とサポートベクターマシーンを用いて人の領域を検出した上で，パーティクルフィルタを適用して人を追跡するものであり，複数の人を追跡することができる．本手法の有効性を検証するために実験を行なったところ，人の検出では約90%，人の追跡では約98%の精度を得ることができた．


Drone Control using Visual Natural Markers

Proceedings of the 2017 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP'17)

本論文では，ドローンの自動制御のために，ビジュアルマーカ―を用いたドローン制御手法を提案した．本手法では，ドローンに搭載されているカメラから得らる映像にマーカ検出と追跡に画像処理を適用し，ドローンの安定した自動飛行及び自動着陸等を容易にした．さらに実際にドローンを飛行させて，挙動を自動制御した実験においても，精度の良いマーカの検出と追跡，さらに安定した飛行と着陸が可能であることを確認することができた．


Safe Driving Support System using Three Onboard Camera

本論文では，3台の車載カメラを用いて，運転者に危険を知らせ，安全運転を支援するシステムを提案した．この手法では，前方カメラでは車線や歩行者，右折車や右折時の直進車，後方カメラでは車線変更の際の後方走行車等を検知した上で，運転者監視で得られる顔の向きと視線の向きと連動させることで安全運転を支援するものである．実際の車載カメラ映像を用いた実験によって，高精度に危険を検知することが可能であることがわかった．


Physical Learning Support using a Smartphone with Image Processing Technologies


Human Action Recognition using Surveillance Cameras and Wearable Sensors


Proposal of Organizing Learning Material with Multiple Perceptions Depending on Preference of Physical Training

Proceedings of The Second International Conference on Electronics and Software Science


Proposal of a Methodology based on Organization of Learning Material including Multiple Perceptions for Knowledge Communication of Physical Training

Proceedings of Gong Show & Young Researcher Contributions on ICKM2016


Intelligent Driver Support Systems: Pedestrian Detection Using HOG Algorithm

Proceedings of the 4th IIAE International Conference on Intelligent Systems and Image Processing


Visually Comprehensible QR Codes via Embedding of Big Logos

Proceedings of IEEE International Conference on Signal and Image Processing


Development of a Gross Motor Skill by Combining Primitive Motions in Repetitive Exercise


3-Dimensional Space Modeling using Kinect

Proceedings of the 34th Chinese Control Conference and SICE Annual Conference2015(CCC&SICE2015)

本論文では，RGB-Dカメラから得られるデータを用いて，3次元モデルを生成する手法を提案した．これは特殊な装置を使ったり，厳密なキャリブレーションしたりすることなく取得した3次元データから，回転不変位相限定相関法と位相限定相関法を用いて高精度な3次元モデリングができる手法である．本稿では，さらにいくつかのシーンに対して，Kinectを用いて得られたRGB-Dデータから，高精度に3次元モデルを生成できることを示した．


Human Action Recognition using Wearable Sensors and Neural Networks

Proc. of 2015 ASCC Emerging Control Techniques for a Sustainable World

Accurate recognition of daily activities could be useful in many fields including health, sports, childcare, and homes for the elderly, etc. In this paper, we propose a human action recognition method using data acquired from wearable sensors and learned using a Neural Network. The data collected from the sensors is processed for features using the Akamatsu transform. The Akamatsu Transform is a signal processing technique that given point, P(i) in a signal, N data points before and after the selected point are used to derive the integral and differential transforms, The Akamatsu Integration is an average of the N data points while the differential is the difference between the integral and the original value. Recently, wearable sensors are emerging as an indispensable method to recognize human actions.


Tire Cracks Detection using Image Processing

Proceedings of the first IEEJ international workshop on Sensing, Actuation, and Motion CONtrol(SAMCON2015)

本論文では，画像処理によって黒色のタイヤに存在する黒色のひび割れを検出する手法を提案した．本手法では，まず取得画像に対して，タイヤの円弧方向に極座標変換することによって直行座標画像に変換する．さらにウェーブレット変換を用いて高周波領域に着目して候補領域として抽出した後，輝度や連続性の特徴量によってひび割れであるかを評価する．さらに実際に撮影されたタイヤのひび割れ画像を用いて，本手法の有効性を検証した．


Feature Extraction from Wearable Sensors for Human Action Recognition

Proc. of 2015 RISP International Workshop on Nonlinear Circuits and Signal Processing

Recently, due to the advancement in wireless sensor technology, wearable sensors are emerging as an indispensable method to recognize human actions. In this paper, data collected from such sensor is processed for features using the Akamatsu transform. The features extracted are to be used later to recognize human action using Deep Neural Networks. The Akamatsu Transform is a signal processing technique that given point, P(i) in a signal, N data points before and after the selected point are used to derive the integral and differential transforms, The Akamatsu Integration is an average of the N data points while the differential is the difference between the integral and the original value. Promising features were extracted for action recognition.


Elderly Monitoring System using Surveillance Cameras

Proceedings of the 2015 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing(NCSP'15)

本論文では，室内に設置された監視カメラから得られる映像を用いて，行動を解析する手法を提案している．この手法では，高齢者養護施設や高齢者独居住居に設置されたカメラ映像から，主に高齢者の行動を認識するもので，転倒，作業，視聴，運動，休憩，冷蔵庫等の開け閉め，静止，移動などの行動を認識して，もし危険があれば通知することができる．さらに実際に製作した簡易的な部屋における実験によって，本手法の有効性を検証した．


Input Interface using Air Character Recognition

本論文では，空中に書かれたAir文字を認識する手法を提案した．本手法は紙やタッチパネルに書かれた文字ではなく，空中に指で書いた文字をカメラ映像によって，その指の動きを追跡することでその文字を認識する手法である．本手法ではカタカナで書かれた文字を認識することが可能だけでなく，消去，リセット，決定などを認識する実用的な手法となっている．さらに実際に空中に書かれた文字を認識する実験で，本手法の有効性を検証した．


Road Surface Marking Recognition Using Neural Network

Proceedings of the2014 IEEE/SICE International Symposium on System Integration(SII2014)

In recent years, the number of traffic accidents has rapidly increased. There are various reasons for this, but as one of the most common one is the carelessness of the driver. Therefore, we aim to decrease the number of traffic accidents by automatically recognizing the road signs and informing the driver accordingly. In the proposed method, at first, we use the edge information and after transform it, we can extract the target road area. The road marking candidate is then extracted and recognized using a neural network.


Indoor Human Action Recognition using Multi Camera

Proceedings of the IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP'14)

In this paper, we propose a method for recognition of human actions using multi cameras especially for the detection of suspicious humans. In human region detection, we use background difference and HOG features, with the learning data for HOG made by AdaBoost. We decide whether the detected human is moving or not using the distance of the detected area between frames. We also estimate the direction the human is facing using the width of detection, and finally recognize the standard action using the height measured from the direction. In addition, we recognize suspicious action using duration of detection and presence of abandoned object. Recognition accuracies for walking and stop actions are about 87% , for running action about 54%, for sitting about 96%, for desk working about 83%, and falling about 88%.


Human Action Recognition using Normalized Cone Histogram Features

Proceedings of the IEEE Symposium Series on Computational Intelligence (CIMSIVP 2014)

In this paper, we propose a normalized cone histogram features method to recognize human actions in video clips. The cone features are extracted based not on the center of gravity as is common, but on the head position of the extracted human region. Initially, the head, hands and legs positions are determined. Thereafter, the distances and orientations between the head and the hands and legs are the extracted and employed as the features. The histogram's x-axis represents the orientations and the y-axis the distances. To make the method invariant to human region sizes, the features are normalized using the L2 normalization technique. The classification method used was the perceptron neural network. We conducted experiments using the ucf-sports-actions database to verify the effective ness of our approach. We achieved an accuracy of about 75% on a selected test set.


A GA - ACO Hybrid Algorithm for the Multi - UAV Mission Planning Problem

Proceedings of the 14th International Symposium on Communication s and Information Technologies(ISCIT2014)

Multi-UAV mission planning is a combinational optimization problem, that aims at planning a set of paths for UAVs to visit targets in order to collect the maximum surveillance benefits, while satisfying some constraints. In this paper, a genetic algorithm and ant colony optimization hybrid algorithm is proposed to solve the multi-UAV mission planning. The basic idea of the proposed hybrid algorithm is replacing the bad individuals of the GA's population by new individuals constructed by ant colony algorithm. Also, an efficient recombination operator called path relinking is used for mating. A population partition strategy is adopted for improving the evolving efficiency. Experimental results suggested that the proposed hybrid algorithm can solve the test instances effectively in a reasonable time. The comparison study with several existing algorithms shows that the proposed algorithm is competitive and promising.


Vehicle Extraction from Aerial Images Captured using an UAV

Proceedings of the 2014 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing(NCSP'14)


Human Action Recognition using Multi Cameras

Suspicious person must be detected at important sensitive institutions like airports, train stations, sports arenas, etc. to maintain safety at these locations. In this paper, we propose the method for recognition of human actions using multi cameras for detection of suspicious persons. In the process for detecting human, we use background difference and HOG features, leaning data for HOG is made by AdaBoost. We judge whether the detected person is moving or not using the distance of the detection area between frames. We also estimate the direction the person is facing using width of detection, and finally recognize the standard action using attitude measured from the direction. In addition, we recognize suspicious action using duration of detection and presence of left object. Recognition accuracies for walking and stop actions are about 87% , for running action about 54%, and for sitting about 96%.


Japanese Road Signs Recognition using Neural Networks

Recently, traffic accidents are increasing because of driver's inattentiveness to the road signs. In this work, in order to assist drivers, we propose a recognition system for road signs using neural networks. We first segment road sign regions using color and shape features to get the location information. The initial sign recognition uses template matching. However, since this did not produce satisfactory results, a neural network is used to learn and recognize the signs. A variety of features are considered for training the neural network. The experimental results show that this recognition method is effective for online road sign recognition.


Hand Written Character Recognition using Star-Layered Histogram Features

Proceedings of SICE Annual Conference 2013

In this paper, we present a character recognition method using features extracted from a star layered histogram and trained using neural networks. After several image preprocessing steps, the character region is extracted. Its contour is then used to determine the center of gravity (COG). This CoG point is used as the origin to create a histogram using equally spaced lines extending from the CoG to the contour. The first point the line touches the character represents the first layer of the histogram. If the line extension has not reached the region boundary, the next hit represents the second layer of the histogram. This process is repeated until the line touches the boundary of the character's region. After normalization, these features are used to train a neural network. This method achieves an accuracy of about 93% using the MNIST database of handwritten digits.


Japanese Road Signs Recognition Using Neural Networks

Proceedings of the International conference on Instrumentation, Control, Information Technology and System Integration(SICE Annual Conference 2013)

The Society of Instrument and Control Engineers

Recently, traffic accidents are increasing because of drivers inattentiveness to the road signs. In this work, in order to assist drivers, we propose a recognition system for road signs using neural networks. We first segment road sign regions using color and shape features to get the location information. The initial sign recognition uses template matching. However, since this did not produce satisfactory results, a neural network is used to learn and recognize the signs. A variety of features are considered for training the neural network. The experimental results show that this recognition method is effective for online road sign recognition.


Automatic Detection of Suspicious Objects Using Surveillance Cameras

Suspicious objects must be detected at important sensitive institutions like airports, train stations, sports arenas, etc. In this paper, we propose a method for automatic detection of suspicious object using a surveillance camera. Person and object areas are extracted using the current frame, previous frame and background. Suspicious object is detected using Histograms of Oriented Gradients (HOG) feature detection method. These features are then learned using the AdaBoost algorithm. Using data collected in our laboratory the system achieved an average accuracy of 85% for 3 types of objects while operating in real time.


Periodic Re-optimization based Dynamic Branch and Price Algorithm for Dynamic Multi-UAV Path Planning

Proceedings of the 2013 IEEE International Conference on Mechatronics and Automation(IEEE ICMA2013)

In this paper, a dynamic branch and price algorithm with a periodic re-optimization framework is proposed to solve the dynamic Multi-UAV path planning problem. By partitioning the planning horizon into a series of time slices, the dynamic problem is transformed into a series of static problems. We then use the adjusted branch and price algorithm to solve each static problem. The prominent characteristic of this algorithm is to improve the solving efficiency at each time slice by succeeding and adjusting the columns from the last time slice. According to the experimental results, the proposed dynamic branch and price algorithm with a periodic re-optimization framework can solve the test instances effectively.


Face Recognition Approach Based on Hyhrid Algorithm

Proceedings of the 2013 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing(NCSP'13)

Face recognition algorithm is a very promising technique in biometric authentication. However, the recognition precision can be affected by many factors, such as feature extraction method and classifier selection. In this paper, a novel algorithm for face recognition is presented. First we detect the face region using an improved AdaBoost algorithm. Second, we extract the appropriate features of the face by wavelet decomposition, and compose the face feature vectors as input to SVM. Third, we train the SVM model by the face feature vectors, and then use the trained SVM model to classify the human face. Finally, we present a face recognition system that can achieve high recognition precision and fast recognition speed in practice. In the training process, three different kernel functions are adopted: Radial basis function, Polynominal and Linear kernel function. Experimental results indicate that the proposed method can achieve recognition precision of 96.78 percent based on 96 persons in Ren-FEdb database.


Face Recognition Algorithm Using Wavelet Decomposition and Support Vector Machines

Proceedings of International Symposuim on OptoMechatronics Technologies(ISOT'12)

Face recognition algorithm is a very promising technique for biometric authentication. However, the recognition precision can be influenced by many factors. In this paper, a novel algorithm for face recognition is presented according to the advances of wavelet decomposition technique and Support Vector Machines (SVM) model. The extracted features from human images by wavelet decomposition are less sensitive to the facial expression variations. As a classifier, the SVM is provided with high generation performance without a transcendental knowledge. First, we detect the face region from the human face using an improved AdaBoost algorithm. Second, we extract the appropriate features of human face by wavelet decomposition, and compose the extracted features vectors to new feature vectors. Third, we train the SVM model by new face feature vectors, and then use the trained SVM model to classify the human face. In the training process, three different kernel functions are adopted: Linear kernel function, Polynomial kernel function and Radial basis function. Finally, we present a face recognition system that could achieve high recognition precision and fast recognition speed in practice. Experimental results indicate that the proposed method can achieve recognition precision of 96.78 percent based on 96 persons in Ren-FEdb database; from the results, our higher than that by SURF and other approaches.


Building Cracks Detection Using Unmanned Helicopter Images

Proceedings of 8th International Conference on Intelligent Unmanned Systems(ICIUS 2012)

Recently, numerous complicated high rise buildings are being constructed. However, it is difficult to inspect such buildings for structural damage regularly. In this research, we propose a system that can safely inspect such building using images captured by an unmanned helicopter.A panorama image was generated from the consecutive-numbered image acquired from the moving object, and a system which detects cracks in the image was developed. From now on, improvement in the accuracy of crack detection is aimed at.


Object Recognition from Aerial Rescue Robots

International Technical Conference on Circuits Systems Computers and Communications


Feature Extraction For Human Recognition Based On Bifurcation Points Using Genetic Algorithms

本論文では，遺伝的アルゴリズムを用いた分岐点に基づく人物認識のための特徴抽出手法を提案した．画像処理において，人物を認識する手法は様々開発されてきたが，人物はそれぞれ体形が異なり，さらに姿勢も変化するため，精度をあげることが難しい．そこで本手法では，交叉や突然変異などを繰り返しながら最適解を求める遺伝的アルゴリズムを用いることで人物認識のための特徴量を抽出する．


Tired State Recognition during Sports using Image Processing


A New Approach for Counting Passersby utilizing Space-time Images

ICDIMSP 2012 : International Conference on Digital Image and Multidimensional Signal Processing

Counting people and observing their movements are essential processes for security, organizational administration, and improving corporate business results. This paper presents amethod for automatically counting passersby using virtual-vertical measurement lines. Recognizing a passerby and generating space-time images are processed by animage sequence obtained from a USB camera. Space-time imagery represents recognizable regions surrounding each personwhich are processed fromlabeling-based segmentation. In this study, a space-time image represents the time as pixel-distance which is used to support the algorithm in order to achieve highly accurate counting. Human regions are detected by space colors based on template matching to select the optimal direction and speed. Whether the shape represents only one or two passersby can be ascertained using the relationship between the passerby speed and the human-pixel area. To verify the effectiveness of the proposed method, we performed experiments using a side view camera placed near the entrance. From the experiments, counting and direction accuracy were achieved 97%.


Reliable Human Recognition and Authentication using Retina Images

Proceedings of 18th Korea-Japan Joint Workshop on Frontiers of Computer Vision(FCV2012)

In this paper, we proposed a method of authenticating humans using retina images. In this work, we report on the preprocessing required before recognition ins performed. First, we extract only the blood vessel regions from a retina image using the HSV color space. The extracting method is the variable threshold method because of the varying color intensities in the image. After that, several methods including smoothing, sharpening, image composition, labeling and thinning are applied. We achieve about 98% blood vessel extraction rate using 20 retina images. We hope that the resulting image can then be used for feature extraction which can implement human authentication at high recognition rates.


An Eye Location Estimation Algorithm based on HSV Color Space and Template Matching

Proceedings of 18th Korea-Japan Joint Workshop on Frontiers of Computer Vision

An accurate eyes location method based on skin color using template matching is proposed in this paper. First the H channel in HSV spaces is used to characterize the colors range for skin detection, and roughly locating the face regions. Then an average synthetic eye template is used to locate the eyes. Experimental results indicate that the method based on HSV color space and template matching was proved accurate and fast. It is even unnecessary to do complex pre-processing and extra processing. The proposed method improves the locating speed and accuracy with better robustness. Experimental results also show that the proposed method could correctly locate the eye part in expressionless and in faces various expressions.


Abandoned Luggage Detection using a Dynamic Background Model and Earth Movers Distance


Robust Facial Features Tracking in Video Sequences using Efficient Template Matching

In this paper we proposed the method of detection and tracking of facial features like eyes, nose and mouth in image sequences using efficient template matching. Facial features tracking can improve human-computer interaction and benefit video surveillance. However, this is not a trivial task because of face size and orientation variations etc. This work presents a real time system for detection and tracking of facial features in video sequences. A skin-color model to segment face candidate regions in the image is used. The presence of absence of a face in each region is verified by means of an eye detector, base of an efficient template. Once a face is detected, the pupils, nostrils and lip corners are located and these facial features are tracked in the image sequence. The proposed system is efficient in tracking a mobile person in real time under varying conditions such as illumination, different movement and rotation.


A People Counting Method using Measurement Lines

It is important to count the number of people entering or leaving various business premises. This is because by knowing the number of people entering and leaving, strategies to increase sales or ease traffic congestion can easily be formulated. This work proposes extraction of people from a camera, and develops a method of counting them using multiple virtual measurement lines.


BCI Based on Single EEG Used Simple Electroencephalograph


Operation Improvement of Indoor Robot by Gesture Recognition

Recently, the demand for the indoor robots has increased. Therefore, increased opportunities for many people to operate the robots have emerged. However, for many people, it is often difficult to operate a robot using the conventional methods like remote control. To solve this problem, we propose a robot operation system using the hand gesture recognition. Our method pays attention to the direction and movement of the hand. We were able to recognize several gestures in real-time.


Background Updating for Abandoned Luggage Detection at Train Stations


A Simple Interface for Mobile Robot Using Motion Stereo Vision

In remote control, user interfaces are very important and it is desired that they have intuitive operability and be simple systems. However, presently, most of interfaces have not satisfied these needs. Therefore, we propose a simple interface to control mobile robots equipped with a camera by instructing it to move to a desired location using the image set to a screen. For the robot to control its movement, it needs to calculate the three-dimensional information of the instructed point. In this work, we calculate the three-dimensional position using motion stereo vision, and perform experiments using a wheeled mobile robot with a single camera in a real environment.


Gait Features Extraction Method using Image Processing

Proceedings of 17th Korea-Japan Joint Workshop on Frontiers of Computer Vision(FCV2011)

It is important to observe a person behavior in various scenes using a simple system. Therefore, in this paper, we propose a method to distinguish people by differences of the characteristics of walking.(gait) This method can acquire the characteristic of walker using the head, shoulder, hands and feet. Some experimental results that indicate the effectiveness of the proposed method are also reported.


A Method for Counting Passersby using Time-Space Image

Recently, there are many serious crimes happening in many institutions and commercial areas. Therefore, security and protection for those areas are needed. This paper proposes an automatic method for counting passersby using horizontal measurement line to solve this serious problem. In this paper, time and direction information are used to count passersby. Therefore, time-space image and measure the speed of each passerby with determination of their distance are applied. In our experiment, the camera is fixed on the front of the hall. A PC is connected to the camera with a frame rate 17 frame per second. Finally, our experimental results prove the effectiveness of our method.


Recognition of a person's state using FG Vision Sensor

It is effective to observe a person's state while protecting individual's information. FG Vision Sensor is able to acquire three-dimensional information and it can thus offer confidentiality. It is appropriate because the processing speed, when used in a monitoring system, is fast. In this research, we propose to recognize a person's state by using FG vision sensor.Some experimental results that indicate the effectiveness of the proposed method are also reported.


Facial Features-Based Method for Human Tracking

The detection and tracking of faces and facial features in video sequences is a fundamental and challenging problem in computer vision. This research area has many applications in face identification systems, model-based coding, gaze detection, human-computer interaction, teleconferencing, etc. This work presents a real time system for detection and tracking of facial features in video sequences. A skin-color model to segment face candidate regions in the image is used. The presence or absence of a face in each region is verified by means of an eye detector, based on an efficient template. Once a face is detected, the pupils, nostrils and lip corners are located and these facial features are tracked in the image sequence. The proposed system is efficient in tracking a mobile person in real time under varying conditions such as illumination, different movement and rotation.


Improving mobility for blind persons using video sunglasses

Proceedings of 17th Korea-Japan Joint Workshop on Frontiers of Computer Vision

Blind people navigate safely through a familiar room based on strong expectations about the location of objects. If something has been moved, added or removed, it can present a difficulty and potentially a danger. Human eyes are one of the most important body parts that help humans to understand and interact with their surroundings. Most learning and recognition of objects around us is accomplished using the eyes. Given the recent advancement of imagery systems and ever increasing processing power of microprocessors, a machine vision aiding system for the blind can be a reality. In the initial system we propose a system consisting of camera equipped sunglasses to capture the images and pattern recognition to automatically detect Braille tiles to aid mobility of blind persons. The information obtained is passed to the subject using audio messages.


Proceedings of 8th edition of France-Japan and 6th Europe-Asia Congress on Mechatronics

In this paper, we propose a method to realize virtual darts playing using image processing. A dart is composed of various colored parts, including the shaft and flights. Using these color features and extracting the necessary values while the dart is in motion, it is possible to perform trajectory calculations. These trajectory parameters can then be used to simulate virtual darts. Some experimental results that indicate the effectiveness of the proposed method are also reported.


Driver Alcohol Consumption Detection System using EEG

Proceeding of 2010 International Workshop on Nonlinear Circuits, Communication and Signal Processing NCSP'10


Biped Robot Walking on Uneven Surfaces using Reinforcement Learning

Recently, reinforcement learning is one of the approaches focused on to achieve adaptive motion in unknown environment. However, most existing approaches require a large number of trials, which becomes one the major problems when the method is applied to an actual robot. Therefore, in this study, we propose an approach that attains low calculation cost to adapt the robot to unknown environments using reinforcement learning. To increase learning accuracy and efficiency, three patterns of rewards are tried and compared for verification. As a result, 70% success rate walking on small uneven surface was achieved


A Simple Interface for Mobile Robot using Motion Stereo Vision

Recently, various kinds of robots are present close to our living environment from their original limited usage in factories, etc. Especially, remote-controlled robots with mobile actuator are being put to practical use for some purposes such as conveying systems, home and office security etc, because the use of remote-controlled robots is easier than autonomous robots. These mobile robots require a controller. In this work, we develop a simple interface for mobile robots. To uses this interface, the users need only point to the position on a screen where they want the robot to move to. After that, the robot controls its own movement between the current position and the target position. To autonomously move to the selected point, the robot needs to calculate the direction and distance of the target from its own position. To calculate direction, we use a simple camera model and pixels surrounding the target point to extract a template. Template Matching is then used to recognize the point during movement. Our interface can forecast movement zone of template image, because it controls movement of the robot. To calculate distance, we use Motion Stereo Vision (MSV). We have two reasons for using this method. First, MSV needs only one camera therefore cost is reduced, and secondly, MSV involves camera movement which is well suited for the mobile robot we are constructing.


Music Classification using Sensitive Words

In this paper, we propose an automatic sensitive word score detection method for a music retrieval system. The proposed method consists of two parts, which are a feature extraction part and a score detection part. The score detection part detects a sensitive word score of each music. In this paper, we use an impression represented by 7 levels for the sensitive word score and the Multi-Layer Neural Networks (MLNNs) are used for detector of sensitive word score. Moreover, we investigate the effective features to detect the sensitive word score. To evaluate the proposed method, we conduct a sensitive word score detection experiment.


Detection of Abandoned Objects from Fluctuating Environment Using Active Background Subtraction

This paper proposes a new method to detect abandoned objects with a focus on Active Background Subtraction. Some estimate background models for dealing with fluctuating environment are utilized. These models are expressed in the HSV color model for more easily removable shadows and adaptive illumination changes. Using these models for background subtraction the object areas from a background image can be divided. In addition, some edge images that are generated from an input image and the value of difference between background models and the input image are used. This method can fix the object area size and rule out shadows as a candidate for abandoned objects. Finally we make a decision regarding the abandoned objects on the basis of displacement and presence time. Preliminary experiments show that the present method is effective for detecting abandoned objects.


Improvement Algorithm for Approximate Incremental Learning

This paper presents an improved algorithm of Incremental Simple-PCA. The Incremental Simple-PCA is a fast incremental learning algorithm based on Simple-PCA. This algorithm need not hold all training samples because it enables update of an eigenvector according to incremental samples. Moreover, this algorithm has an advantage that it can calculate the eigenvector at high-speed because matrix calculation is not needed. However, it had a problem in convergence performance of the eigenvector. Thus, in this paper, we try the improvement of this algorithm from the aspect of convergence performance. We performed computer simulations using UCI datasets to verify the effectiveness of the proposed algorithm. As a result, its availability was confirmed from the standpoint of recognition accuracy and convergence performance of the eigenvector compared with the Incremental Simple-PCA.


Palm print Recognition Based on Local DCT Feature Extraction

In this paper we present a method which extracts features from palmprint images by applying the Discrete Cosine Transform (DCT) on small blocks of the segmented region of interest consisting of the middle palm area. The region is extracted after careful preprocessing to normalize for position and illumination. This method takes advantage of the well known capability of the DCT to represent natural images using only a few coefficients by performing the DCT on each block. After ranking the coefficients by magnitude and selecting only the most prominent, these are then concatenated into a compact feature vector that represents each palmprint. Recognition and verification experiments using the PolyU Palmprint Database show that this is an effective and efficient approach, with a recognition rate above 99 % and Equal Error Rate (EER) of less than 3 %.


Wrist EMG Signals Identification using Neural Network

Recently, researches of artificial arms and pointing devices using electromyogram (EMG) have been actively done. However, EMG is usually measured from a part with comparatively big muscular fibers such as arms and shoulders. Therefore, if we can recognize wrist motions using EMG which was measured from the wrist, the range of application will extend furthermore. Moreover, it is predicted that convenience in putting on and taking off the electrode improves. Therefore, we focus on EMG measured from the wrist. In this paper, we aim the construction of wrist EMG recognition system by using fast statistical method and neural network.


Out-of-Focus Blur Image Restoration using the Akamatsu Transform

In this paper, a new image restoration approach using the Akamatsu transform is presented. In the method, by using repeated simple calculations based on the Akamatsu transform, an out-of-focus blurred image can be restored and sharpened. Out of focus is a major problem bothering many people in photography, especially the amateurs. There exits some solution to this problem on both the hardware and software sides. However, none offers a perfect solution. The Akamatsu transform can easily be embedded into hardware to offer faster processing due to its simplicity. Although, initially proposed for speech processing, this paper show the effectiveness of the transform in image processing. The Akamatsu transform is a combination of integral and differential transforms. The algorithm can be effective tools for image restoration in realtime image processing.


Biped Robot Walking Control using Image Processing

In this paper, a vision system for controlling an autonomous biped robot is presented. Robots need sensors to understand circumstances. There are several types of sensors. Recently, a camera is rapidly becoming the sensor of choice due to the remarkable improvement in computer technology especially in processing power. However, there is the blurred image problem because of camera shake due to robot's walking. Our method focuses on the use of image processing to control a humanoid robot's walking. We fix a camera on a biped robot, and input the images from the camera to PC for processing. The results of the image processing are sent to the robot for control immediately. These real-time image processing and control systems give us an efficient application on future robots. In the initial experiment the objective is to control robot walking upstairs.


A Circle-Based Region-Of-Interest Segmentation Method for Palmprint Recognition

This paper presents a novel method for optimal region-of-interest (ROI) segmentation for palmprint feature extraction based on the largest inscribed circle. This ensures that the optimal amount of features can be extracted, unlike square-based methods which exclude a substantial area on the outside region of the palmprint image. After position normalization, the middle portion of the palmprint is searched to determine the center from which the largest inscribed circle can be extracted. The circular area is then unwrapped into a fixed-size rectangular strip which is further preprocessed to remove redundancies and then split into seven equal square sub-images. A layered approach is then adopted during the matching stage where each square is successively matched and polled to produce a matching score. Experiments are performed using the `PolyU Palmprint Database' and results show that this is a viable method for palmprint feature extraction, with a recognition rate of above 90% obtained.


Identification of Wrist EMG Signals using Dry Type Electrodes

Recently, researches of artificial arms and pointing devices using electromyogram(EMG) have been actively done. However, EMG is usually measured from a part with comparatively big muscular fibers such as arms and shoulders. Therefore, if we can recognize wrist motions using EMG which was measured from the wrist, the range of application will extend furthermore. However, when we use the wrist EMG, there are problems that the individual difference is large and its repeatability is low and so on. In this paper, we aim the construction of wrist EMG recognition system that is robust to these problems.


Improvement of Evolutionary Eye Sensing with Buccal Region",


Relation Between Mental Change and EEG When Performing Trivial Tasks

Recently, due to long time usage of computers and Visual Display Terminals (VDTs), mental stress and related illnesses have increased tremendously in today's contemporary society. If a computer based method to diagnose stress becames available, it would go a long way in reducing stress illnesses. Hence, it is thought that quantification of stress is important. In this paper, we study the relationship between stress and electroencephalogram (EEG) signals. We studied the effect of stress (the pysiological parameter) when the brain is doing trivial tasks by measuring the brain waves of a subject before and after doing such a task. We apply principal component analysis (PCA) and linear discriminant analysis (LDA) to characterize the change in each component, extract it and discriminate using a neural network (NN). The trivial task used in this experiment is asking the subject to pick and move beans from one plate to another using chopstick. As a result, it was found that PCA is an effective analysis method in this research. Therefore, it can be said that even trivial task have an effect on the brain that may contribute to stress.


Incremental Learning Method for Biological Signal Identification

Proc. of 13th International Conference on Biomedical Engineering (ICBME2008)


Fast statistical learning with Kerne-Based Simple-FDA

Proc. of IEEE International Conference on Signal Image Technology and Internet Based Systems

In this paper, new statistical learning algorithms with kernel function are presented. Recently, iterative learning algorithms for obtaining eigenvectors in the principal component analysis (PCA) have been presented in the field of pattern recognition and neural network. However, the Fisher linear discriminant analysis (FLDA) has been used in many fields, especially face image analysis. The drawback of FLDA is a long computational time based on a large-sized covariance matrix and the issue that the within-class covariance matrix is usually singular. In order to overcome this difficulty, we proposed the feature generation method simple-FLDA which is approximately derived from geometrical interpretation of FLDA. This algorithm is similar to simple-PCA and does not need matrix operation. In this paper, new statistical kernel based learning algorithms are presented. They are extended versions of simple-PCA and simple-FLDA to nonlinear space using the kernel function. Their preliminary simulation results are given for a simple face recognition problem.


Interactive Interface Using Evolutionary Eye Sensing

Proceedings of the 11th IASTED International Conference Intelligent Systems and Control (ISC 2008)


Classification of Fingerprint Images into Individual Classes Using Neural Networks

Proceedings of the 34th Annual Conference of the IEEE Industrial Electronics Society


Incremental Algorithm of Discriminant Analysis

This paper presents a new incremental learning algorithm named Incremental Simple-FLDA. It is the algorithm that improved Simple-FLDA to update an eigenvector by using incremental data. Simpe-FLDA is an approximation algorithm of the linear discriminant analysis where an eigenvector can be calculated by a simple repeated calculation. Using this proposal algorithm, the eigenvector is approximately obtained whenever incremental data is added one by one. We carry out computer simulations on personal authentication that uses face images by incremental learning to verify the effectiveness of this algorithm. As a result, the effectiveness of proposal algorithm is shown.


Incremental Learning Method of Simple-PCA

n this paper, we propose an incremental learning algorithm named Incremental Simple-PCA. This algorithm is added an incremental learning function to the Simple-PCA that is an approximation algorithm of the principal component analysis where an eigenvector can be calculated by a simple repeated calculation. Using the proposed algorithm, it allows to update faster the eigenvector by using incremental data. To verify the effectiveness of this algorithm, we carry out computer simulations on personal authentication that uses face images and wrist motion discrimination using wrist EMG by incremental learning. As a result, we can confirm the effectiveness from the aspects of accuracy and a computing time by comparing the Incremental PCA that gave the incremental learning function to the conventional PCA.


Facial Gesture Simulation for a Single Image using Image Warping


Identification of Music According to Singer

We propose a singer identification method using a singer model for retrieving the music data. For the speaker models, we use Gaussian Mixture Models (GMMs) and Vector Quantization (VQ) centroids. To evaluate the proposed method, we conducted the speaker identification experiments. Experimental results show that the correct identification accuracy is 65.0% under the condition that the GMMs are used for the speaker models and the 81.9% under the condition that the VQ centorids are used for the speaker models. We also experimented changing the training data to estimate the best parameter for GMM. The correct identification accuracy is 50.0%, under the condition that training data is 1 and the 95.0%, under the condition that training data is 8. Owing to result of GMM, it has been understood that accurate parameter estimation becomes possible by increasing the number of training data.


Relation between mental change and EEG when doing Trivial Tasks

Recently, due to long time usago of computers and Visual Display Terminals (VDTs), mental stress and related illnesses has increased tremendously in today's contemporary society. If a computer based method to diagnose stress were available it would go a long way in reducing stress illnesses. Hence, it is thought that quantification of stress is important. In this research, we study the relationship between stress and electroencephalogram (EEG) signals. We studied the effect of stress (the pysiological parameter) when the brain is doing trivial tasks by measuring the brain waves of a subject before and after doing such a task. We then apply simple principal component analysis (Simple-PCA) to characterize the change in each component, extract it and discriminate using a Neural Network (NN). The trivial task used in this experiment is asking the subject to pick and move beans from one plate to another using chopstick. A recognition accuracy of about 80 % was achieved.


Face Information Processing by Fast Statistical Learning Algorithm

In this paper , we propose a new statistical learning algorithm. This study quantitatively verifies the effectiveness of its feature extraction performance for face information processing. Simple-FLDA is an algorithm based on a geometrical analysis of the Fisher linear discriminant analysis. As a high-speed feature extraction method, the present algorithm in this paper is the improved version of Simple-FLDA. First of all, the approximated principal component analysis (learning by Simple-PCA) that uses the mean vector of each class is calculated. Next, in order to adjust within-class variance in each class to 0, the vectors in the class are removed. By this processing, it becomes high-speed feature extraction method than Simple-FLDA. The effectiveness is verified by computer simulations using face images.


Feature Generation for Face Recognition by Fast Statistical Learning Algorithm

In this paper,we proposed a new statistical learning algorithm. This paper quantitatively verifies the effectiveness of the feature extraction performance for face information processing. Simple-FLDA is an algorithm based on a geometrical analysis of the Fisher Linear DIscriminant Analysis. As a high-speed feature extraction method, this algorithm is improved version of Simple-FLDA. First of all, the approximated proncipal component analysis (learing by Simple-PCA) that uses the mean vector of each class is calculated. Next, to adjuast within-class variance in the class to 0, the vectors in the class are removed. By this processing, it becomes hi-speed feature extraction method than Simple-FLDA. The effectiveness is verified by computer simulations using face images.


Enhancement of Palmprint Images using an Optimized Hexagonal Mulitilayer Perceptron Neural Network

The image capture and storage stages of palm and other biometric image applications is susceptible to various forms of noise due to varying lighting conditions and the type of image-input process employed. In this paper, we present a method for improving palmprint image contrast and detail using hexagon-shaped fully connected feedforward neural network with a single hidden layer, similar to the ones that have been used in literature for prediction and regression applications. It is trained, using Outputweight- optimized Back Propagation (OWO-BP), to remove various forms of noise added to the input images, producing an image where the fine details of the principal lines and wrinkles are highlighted. The training is performed using sub-images of the 32x32 pixel gray-scale images of the central palm area. This method offers a viable, fast-learning and one-stop alternative to the application of spatial and spectral image processing techniques such as unsharp masking, spatial filtering and FFT-based sharpening algorithms. The network can also be applied to image restoration problems, if the noisy input image is viewed as a degraded version of the target, enhanced image.


Fast incremental learning algorithm in pattern recognition

In a field of pattern recognition, researches on feature extraction and dimension reduction are actively done by using methods of the principal component analysis (PCA) and the linear discriminant analysis (LDA), etc. to data with a lot of attributes. On the other hand, there is an algorithm named Simple- PCA to solve such problems. This is an approximation algorithm of PCA. The algorithm that added the learning function to this Simple-PCA has not been presented yet. In this paper, we propose an Incremental Simple-PCA that added an incremental learning function to the Simple-PCA. Moreover, we carry out the computer simulation on personal authentications that use face images by incremental learning to verify the effectiveness of the proposal algorithm.


Multiple Faces Detection in Real Time using Neural Networks

Proceedings of the 6th International conference on Computational Intelligent, Man Machine Systems and Cybernetics


Biological Signal Recognition by Fast Statistical Learning Algorithm

This paper presents an improved statistical learning algorithm, which is approximately derived from geometrical interpretation of the Fisher linear discriminant analysis (FLDA). Generally, FLDA has to carry out minimization of a within-class variance. However the inverse matrix of the within-class covariance matrix could not be obtained. In order to overcome this difficulty, a new iterative feature generation method, the simple-FLDA was proposed by authors. In this paper, a further improvement algorithm is introduced into the simple-FLDA and its effectiveness is demonstrated for biological signal recognition problems.


Character Recognition of Malaysian Vehicle License Plate by Neural Networks

This paper presents a method to recognize characters of vehicle license plate in Malaysia by using image processing techniques and a neural network (NN). Vehicle license plate recognition is one of important techniques that can be used for the identification of vehicles all over the world. There are many applications such as entrance admission, security, parking control, airport or harbor cargo control, road traffic control, speed control, toll gate automation and so on. In this paper, techniques for a dynamic image analysis including vehicle detection, license plate segmentation, and character extraction are utilized, which include peculiar knowledge on Malaysian license plate. NN is then used for separated character recognition. The training of NN is carried out using features with boundary coding and gradient components. Computer simulations show that character segmentation and recognition can be effectively carried out using the present method.


Construction of Online Tuning System based on Wrist EMG

Recently, studies of artificial arms and pointing devices using ElectroMyoGram(EMG) have been actively done. However, the individual variation of EMG is large, and its repeatability is low. Furthermore, EMG is usually measured from a part with comparatively big muscular fibers such as arms and shoulders. Therefore, if we can recognize wrist operations using EMG which was measured from the wrist, the range of application will extend furthermore. In this research, we aim toward the development of a device of wristwatch type that consolidates operational interface of various equipments. In the previous research, we proposed a wrist motion recognition system by using Simple-PCA and Simple-FLDA to reduce the number of inputs to a neural network. However, the recognition accuracy was changed considerably by the individual. Therefore, it is thought that the construction of an online tuning system that can consider the individual variation is necessary. In this paper, we propose an improvement algorithm of Simple-PCA named Incremental Simple-PCA that updates eigenvectors sequentially by adding a new data. Moreover, we try the construction of the online tuning system by using this algorithm.


Incremental learning method in wrist EMG recognition system

In a field of pattern recognition, researches of feature extraction and dimension reduction using the Simple-PCA that is an approximation algorithm of the principal component analysis (PCA) are actively conducted. In such a statistical method, a lot of algorithms that perform incremental learning by using new incremental data exist. For example, there is an algorithm named Incremental PCA for PCA. However, the algorithm capable of performing incremental learning to the Simple-PCA has not been presented yet. In this paper, we propose Incremental Simple-PCA that introduces an incremental learning function to the Simple-PCA. Moreover, the effectiveness of this algorithm is verified in EMG pattern recognition.


Fast Statistical Learning Algorithm for Feature Generatio

This paper presents an improved statistical learning algorithm for feature generation in pattern recognition and signal processing. It is approximately derived from geometrical interpretation of the Fisher linear discriminant analysis (FLDA). Recently FLDA has been often used in many fields, especially face image recognition. The drawback of FLDA is a long computational time based on a large-sized covariance matrix and the issue that the within-class covariance matrix is usually singular. In order to overcome the difficulty of FLDA, a new iterative feature generation method, the simple-FLDA was proposed by authors. In this paper, further improvement is introduced into the simple-FLDA and its effectiveness is demonstrated for preliminary personal identification problem.


Discrimination of Wrist EMG Signals using a Statistical Method

Proc. of 2007 RISP International Workshop on Nonlinear Circuits and Signal Processing

In this study, we aim toward the development of a device of wristwatch type that consolidates operational interface of various equipments. In the previous study, the recognition accuracy was changed considerably by the individual. Therefore, we thought that the construction of an online tuning system that can consider the individual variation was necessary. In this paper, we propose an improvement algorithm of Simple-PCA that changes eigenvectors sequentially by adding data. Moreover, the construction of the online tuning system is carried out by using this algorithm.


Extraction of the Liver Tumor in CT Images by Real-coded Genetic Algorithm

The purpose of this work is the construction of an automatic diagnosis support system for CT images in order to reduce the doctor's load. Toward this end, in this paper, a method to extract liver tumors in CT images using a real-coded genetic algorithm is proposed. Conventionally, a threshold is necessary to extract an object from an image. However, such a method is not effective for CT images because gray scale values are different in each image. Therefore, in this paper, we propose the method for extracting the tumor in the liver from the CT image without the need of a threshold. In this method, a polygon enclosure of the liver tumor is extracted using a GA.


Isolated Syllable Recognition on Mobile Terminal Devices

Proc. of IASTED International Conference on Computational Intelligence

The International Association of Science and Technology for Development

The aim of this work is the establishment of isolated syllable recognition for Japanese language on mobile terminal devices and/or the retry after the failure in speech recognition. The subjects in this study are five Japanese vowels. The experiment was carried out on 100 sets of five Japanese vowels from four men. The experiment for recognition was carried out using the Akamatsu transform. The experiment demonstrated the effectiveness of the proposed technique for recognizing the isolated 100 sets. This paper serves as an introduction on the use of the Akamatsu transform in speech recognition and does not, as yet, compare the results with existing methods.


Recognition of Wrist Motion Pattern by EMG

Recently, studies of artificial arms and pointing devices using ElectroMyoGram (EMG) have been actively done. However, the individual variation of EMG is large, and its repeatability is low. Furthermore, EMG is usually measured from a part with comparatively big muscular fibers such as arms and shoulders. Therefore, if we can recognize wrist operations using EMG which was measured from the wrist, the range of application can extend furthermore. In this study, we aim toward the development of a device of wristwatch type that consolidates operational interface of various equipments. In particular, as an early stage, we propose a wrist motion recognition system. First, we execute the Fourier transform to the signal for feature extraction. Next, we experiment it by using neural networks after the dimensional reduction by using simple-PCA and simple-FLDA to reduce the number of inputs to NN. It was confirmed that the present approach was one of the techniques which were effective in the wrist recognition experiment.


A Simple 3D Edge Template for Pose Invariant Face Detection

Proc. of Knowledge-Based & Intelligent Information & Engineering Systems (KES)


Automatic Extraction of Heart Contour using SNAKES

Recently, as sophisticated medical instruments have been developed, the internal state of a human body has become well-known. In particular, in the examination of heart, non-surgical and non-intrusion ultrasonic diagnostics are generally utilized. However, the diagnosis depends on the doctor's subjectivity and experience. Furthermore, the doctor's burden increases because the contour of the heart is extracted by manual operations. Therefore, the development of a system that offers objective information in addition to a subjective judgment of the doctor by extracting and analyzing quantitative information from image analysis by computers is necessary. In this paper, the contour of the chamber wall is extracted by using an active contour model SNAKES as a preprocessing of the heart disease detection. The contour has been extracted by using SNAKES of the expansion model from the inner surface of heart


Feature Extraction in Listening to the Music Using Statistical Analysis of the EEG

In order to solve stress problems, researchers have studied healing, especially the music therapy. It is mentioned that objective evaluation of the music therapy is an important assignment, and some researchers have tried objective measurement based on physiological change. In this paper, the purpose is extraction of features that may be influenced by the music. We pay attention to EEG (electroencephalogram) as an objective and absolute scale. This paper proposes a method that extracts features of the EEG by the CDA (canonical discriminant analysis). From the result of the experiment, it is suggested that the CDA extracts the features influenced by the individual and the music type


Design of an IR Communication Link for a Computer-Controlled Humanoid Robot

Recently, many humanoid robots can be formed on television programs and public events. In this paper, we pay attention to such robots. Under this background, diverse parts have been developed and many symposiums have been held to share domain knowledge with many engineers. Furthermore, as for the robot technology, we think that a robot which makes humanlike movement and general view has been developed. However, if a multifunction robot were developed, it could work instead of a human and our lifes could be more convenient and less risky. However autonomous action is very difficult to achieve because a robot cannot recognize varying environment all of the time. In this paper, we focus on how to recognize such environments. The environment is recognized using a camera and is learned with a neural network (NN). The autonomous action can be learned by the NN on the robot or transferred from a PC after action learning. In this work, we use the latter method, and then transmit the image that is necessary to control the robot from a PC because the robot cannot acquire the image by itself.


Feature Point Extraction in Face Image by Neural Network

Conventionally, manual operations that specify positions of feature points such as eyes and nose are needed when morphing is carried out for a face image. In this work, the feature points are therefore extracted by using face area detection and a feature points decision methods to automate positional specification of feature points. As a result, the morphing of a face image can be carried out without manually specifying feature points. Face area detection is achieved by a threshold method using the YIQ color system. Feature points decision method extracts feature points by using a 3 layer perceptron type neural network (back-propagation). The attribute of the feature of eyes is defined to be a value of A in the color system LAB. In the same way, the attribute of feature points of the lip is defined as a value of B in the color system LAB. The extraction experiment of feature points was conducted from 120 face images by using the neural network, and the effectiveness of the present method was verified.


Downsized Genetic Algorithm by Automatic Search Domain Control for Lips Detection

Proc. of the International Conference on Soft Computing and Intelligent Systems and International Symposium on advanced Intelligent Systems (SCIS & ISIS 2006)

In this paper, a method of downsizing a genetic algorithm (GA) for lips detection is described. As part of the objectives, we also try to acquire information to represent the lips. GA is very useful for optimization of parameters in complex image matching. When GA is applied to the real-time processing, the search speed is a issue with keeping the accuracy. Using small population is one of the approaches to improve the GA search speed. However, this can cause the low accuracy, because diversity of the population will be lost. In order to avoid this problem, we propose a downsized GA with automatic search domain control. In this paper, the optimization problem is template matching. The target image includes a face of talking person and has significant changes of a whole scene by camera motion. The template image is only one closed mouth and prepared for each environment and person, because of personal use. In simulations, a simple image is used, which is the basis of video sequence. Our proposed method is verified by comparison of a process of evolution between a downsized GA and a standard GA.


Personal Authentication using Fingerprints

Proc. of the International Conference on Soft Computing and Intelligent Systems and International Symposium on advanced Intelligent Systems


Recognizing Parked Vehicles License Plate Using Image Frame-Difference and Template Matching

In this paper, parked vehicles license plate recognition using image frame-difference and template matching is proposed. Image frame-difference is used to extract the vehicle area, reducing the license plate search domain, thereby reducing the total search time. The processes of expansion and reduction are then applied to reduce the noise in the extracted plate domain. Based on the data from 83 vehicles, the most likely plate area is then decided. License plate recognition is carried out using template matching. For each possible character, three templates are prepared. We then performed computer simulations to show the effectiveness of our method. From the result, it was confirmed that the preprocessing method was effective.


Eye Detection and Tracking using Genetic Algorithm

Proceeding of the 4th International Conference on Computing, Communications and Control Technologies


Optimizing Feature Extraction for the Camera Mouse using Genetic Algorithms

World Scientific and Engineering Academy and Society (WSEAS)


Construction of Wrist Motion recognition System

Proc. of 2006 RISP International Workshop on Nonlinear Circuits and Signal Processing

Recently, studies of artificial arms and pointing devices using ElectroMyoGram(EMG) have been actively done. However, the individual variation of EMG is large, and its repeatability is low. Furthermore, EMG is usually measured from a part with comparatively big muscular fibers such as arms and shoulders. Therefore, if we can recognize wrist operations using EMG which was measured from the wrist, the range of application will extend furthermore. In this study, we aim toward the development of a device of wristwatch type that consolidates operational interface of various equipments. In particular, as an early stage, we propose a wrist motion recognition system. First, we execute the Fourier transform to the signal for feature extraction. Next, we experiment it by using neural networks after the dimensional reduction by using Simple-PCA and Simple-FLDA to reduce the number of inputs to NN. It was confirmed that it was one of the techniques which were effective in the wrist recognition experiment.


In this paper, high-speed size and orientation invariant eye tracking in an active scene using template matching and genetic algorithms is proposed. As part of the objectives, we also try to acquire numerical parameters to represent the eye, for example, position, scaling, and rotation angle. The information is useful for many applications, where high performance is required, such as eye gaze detection or estimation for robot perception and mobile devices interfaces. The difficulty in eye tracking is mainly due to motion of the human head and the active scene by free camera motion. Our proposed system is based on template matching using genetic algorithms (GAs). Only one artificial template is used for every human user. Various geometric changes of iris can be supported by genetic algorithms. In this paper, eye tracking system with genetic algorithms is proposed, and selection of the best template image is described. We achieved a eye detection accuracy of 74 % at an average processing time of 0.03 seconds.


Measurement of Skin Texture Using Artificial Neural Network and SOM

In this paper, the purpose is to offer a different diagnostic method for skin texture evaluation using sophisticated skin diagnostic system that a layperson can easily use at home. The experiments for evaluation were carried out using an artificial neural network (NN) and a Self-Organizing Map (SOM) to measure the degree of similarity. The Fast Fourier Transform (FFT) was performed to skin images and the number of skin textures counted used as the input data. Then we use Simple Principal Component Analysis (SPCA) in order for PCA to achieve high speed processing. In this experiment, we use newly SPCA and could obtain effective result comparison with before. Moreover, the mapping images of SOM and SPCA were obtained the result that is nearby positioned the similar image of skin.


Korean Lightweight Font Development using Outline Compression Reduction Function

Proc. of The 12th Korea-Japan Joint Workshop on Frontiers of Computer Vision


Automatic Extraction System of a Kidney Region from CT Images

CD Proc. of 9th International Conference on Mechtornics Technology

In this paper, a kidney region extraction method as a preprocessing of kidney disease detection is proposed. The kidney region is detected based on its contour information that is extracted from a CT image using a dynamic gray scale value refinement method based on the Q-learning. An initial point to extract the kidney contour is decided by training gray scale values along horizontal direction using a Neural Network (NN). Furthermore the kidney contour is corrected by using SNAKES. It is demonstrated that the proposed method can accurately detect the kidney contour from CT images of any patient.


Relevance of the EEG and feeling during listening to the music

In order to solve stress problems, researchers have studied music therapy. In this paper, the purpose is extraction of features that may be influenced by the music. We pay attention to EEG (electroencephalogram) as an objective and absolute scale. This paper proposes a method that extracts features of the EEG by PCA (principal component analysis) and CDA (canonical discriminant analysis). Then we analyze each feature data by NN (neural network). In order to examine whether the proposal system is effective, we try computer simulations for the EEG classification. According to recognition rate by the NN, it was considered that the CDA extracted and classified the features of the EEG better than the PCA.


Automatic Extraction of Left Ventricle by Using X-Ray Photogram

Recently, as sophisticated medical instruments are being developed, the inside state of a human body has become well known. However, doctor's burden becomes heavier because the number of images that are taken with medical instruments per person drastically increases. In such cases, the development of automatic extraction system for the organs is needed. In this paper, we propose the extraction the left ventricle in such images using the Gabor transform and SNAKES. The kind of image processed in this work is the X-ray photograms of the left ventricle by cardiac catheterization.


Contour Extraction of Heart from Ultrasonic Image Using SNAKES

Recently, as sophisticated medical instruments have been developed, the inside state of a human body has become well-known. In particular, in the examination of heart, non-surgical and non-intrusion ultrasonic diagnostics are generally utilized. However, the diagnosis, depends on the doctor's subjectivity and experience. Furthermore, the doctor's burden increases because the contours of the heart are extracted by manual operations. Therefore, the development of automatic diagnostic imaging systems is needed. In this paper, the contour of the entire heart and left ventricle is extracted by using an active contour model SNAKES as a preprocessing of the heart disease detection. The entire heart contour made with SNAKES is used as a control point in the next image to automate contour tracking.


Wrist Motion Recognition System by Neural Networks

We propose a recognition system based on wrist movements by focusing on ElectroMyoGram (EMG), that is, the living body signal generated with voluntary movement of subject muscles as the initial stage for construction of the total operation device. In this paper, we aim for construction of a high-speed and high-accurate EMG recognition system using Fast Fourier Transform (FFT) for feature extraction, Simple-PCA (SPCA) for feature compression, and Neural Networks (NN) for recognition. Furthermore, we select evaluation data using Self-Organizing Map (SOM) for recognition accuracy improvement. From results of computer simulation, it is shown that our approach is effective in the improvement of recognition accuracy.


License Plate Localization Using Template Matching


Feature Generation by Simple-FLDA for Pattern Recognition

Proc. of International Conference on Computational Intelligence for Modelling, Control and Automation

In this paper, a new feature generation method for pattern recognition is proposed, which is approximately derived from geometrical interpretation of the Fisher linear discriminant analysis (FLDA). Recently the FLDA has been used in many fields, especially face image analysis. The drawback of FLDA is a long computational time based on a large-sized covariance matrix and the issue that the within-class covariance matrix is usually singular. Generally FLDA has to carry out minimization of a within-class variance. However in this case the inverse matrix of the within-class covariance matrix cannot be obtained, since data dimension is generally higher than the number of data and then it includes many zero eigenvalues. In order to overcome this difficulty, a new iterative feature generation method, a simple FLDA is introduced and its effectiveness is demonstrated for pattern recognition problems.


Malaysian License Plate Recognition Using Artificial Neural Networks and Evolutionally Computation

Proc. of International Federation for Systems Research


Automatic Facial Gesture Construction Using Image Warping


Face Gesture Recognition Using Artificial Neural Networks

Proc. of The Society of Instrument and Control Engineers (SICE) Annual Conference


Face Detection: Size and Rotation Invariance using Genetic Algorithms

Proc. of 2005 RISP International Workshop on Nonlinear Circuits and Signal Processing

本国際会議論文は，カラー画像中から顔を高速高精度に検出する方法を提案している．特に本方法では，色情報に基づいてYIQ成分の閾値をを用いて顔候補領域を推定し，その後ニューラルネットワークにより顔候補の洗練化を行う．顔判定用のニューラルネットワークは速度を改善するために結合数を低減化させる工夫を行っている．さらに，遺伝的アルゴリズムを用いて，顔のスケール変化，回転に不変な構造を導入して様々な顔を検出できるようにしている．計算機シミュレーションの結果から，本手法の有効性を定量的に検証している．


Automatic Face Metamorphosis in Color Images

本国際会議論文では，カラー顔画像のモーフィングを行うための方法を提案している．特に，顔の制御点(特徴点)を自動的に抽出する方法を考案している．この方法ではエッジ画像を作成し，スムージングを行った後に，エッジ画像の集中具合に基づいて自動的に顔の特徴点(鼻，目の中心，口の両端)を5点抽出している．この制御点に基づいて，ワーピングと色情報の変換に基づいてモーフィングを行う方法である．計算機シミュレーションにより，本手法の有効性を確かめている．


Face Recognition using Genetic Algorithm based Template Matching

Proc. of IEEE International Symposium on Communications and Information Technologies'2004

本国際会議論文は，遺伝的アルゴリズムにより顔検出を効率的に行う方法を提案している．本手法では，テンプレートを2種類(カラーとエッジ画像)用意し，かつ顔の特徴点だけを含むT字型のテンプレートを提案し，使用している．様々な顔画像に対する計算機シミュレーションにより，従来法よりも優れた顔検出精度が得られることを定量的に検証している．


Feature Extraction for Face Detection and Recognition.

Proc. of 13th IEEE International Workshop on Robot and Human Interactive Communication'2004

We propose a facial feature extraction method for face detection and recognition using image segmentation with adaptive thresholds and real coded genetic algorithm guided shape matching. The shapes template is constructed using the average outer edges of the lips and the eyes. Image segmentation is performed using a region growing method, whose seeds are determined using a hybrid method that combines histogram, random and pixel-by-pixel methods. Adaptive thresholds are calculated using color variance. Color spaces used are the YIQ, XYZ and the HIS. Color variance is worked out using square, star and plus kernels.


Recognizing Frontal Faces using Neural Networks.

Proc. of 8th International Conference on Knowledge-Based Intelligent Information & Engineering Systems'2004

In this paper, a neural network based face recognition system is presented. One of the main problems encountered when using neural networks for face recognition is luck of enough training data. This is because, in most cases, only one image per subject is available. Therefore, one of our objectives is to solve the problem of luck of enough data to train neural networks. For each image we ''increase'' the data available by several processes for example, mirroring of the image, using color, edges information, etc. The neural network is trained using structural learning to reduce its size. To represent the face color, the YIQ and the XYZ color spaces are used.


Morphing Face Images Using Automatically Specified Features

In this paper, we present a method using which face images can be automatically warped and morphed. Image warping can be defined as a method for deforming a digital image to different shapes. Image morphing combines image warping with a method that controls the color transition in the intermediate images produced. To morph one image to another, new positions and color transition rates for the pixels in each of the images in the sequence must be calculated. Three processes are involved; feature specification, warp generation and transition control.


Reconstruction of Facial Skin Color in Color Images

Proc. of Knowledge-Based Intelligent Information & Engineering Systems'2003

In this paper, a method to recover and reconstruct skin color from images over or under-exposed to scene illumination is proposed. The skin color regions most susceptible to over-exposure include areas around the forehead, tip of the nose and the cheeks. This method uses a feed-forward neural network and a distance measure method to learn and construct the skin color face image map. The color distance measure method used is the Cylindrical Metric method (CMM). The relationships between neighboring pixels skin color distance and their relative position in a face are learned using the neural network. This information is then used to reconstruct over or under-exposed skin color regions.


Neural Networks and Genetic Algorithms for Learning the Scene Illumination in Color Images

Proc. of IEEE International Symposium on Computational Intelliegnce in Robotics and Automation

This paper proposes the use of neural networks to estimate the scene or camera white balancing illuminant in color images. A real coded genetic algorithm is also used to shirk the size of the neural network input data (thus, a smaller network that will train and run faster), and to identify the areas of a scene that contain the most information about, and therefore best represent the scene illuminant (this offers better data quality thereby improving the accuracy). In addition structural learning with knowledge was incorporated with the back propagation method to further reduce the size of the neural network. The final appearance of the colors in an image depends on many factors. The three main factors are the scene illumination, the illuminant used to white balance the image acquisition device and the properties of the object. While both the camera white balancing illuminant and the properties of the object might be known a prior, it is not always possible to control the scene illumination. In this paper, facial skin color was used as the object interest. Given a visual scene, the proposed method first searches for the presence of at least one face in the scene. If one is found, the genetic algorithm is used to extract the inputs to the neural network, that in turn outputs the estimated scene illuminant. Presently, the proposed method is used to estimated four illuminants in the range 2300K to 6500K.


Identifying Scene Illumination using Genetic Algorithms and Neural Networks

CD-ROM Proc. of 2002 International Conference on Neural Information Processing

The scene illumination that is present when an image is captured plays a major role in the appearance of the colors of the objects in that image. This is because color is not a physical property of objects and therefore, it is not constant under most conditions, for example, changing scene illumination. We target skin color as the object of interest in an image. We used neural networks to learn and identify which of four different types of scene illuminations were present in a given scene. A real coded genetic algorithm was also used to shrink the size of the neural network input data, and to identify the areas of a scene that contain the most information about, and best represent the scene illuminant Once a scene illuminant can be identified, it can go a long way in helping to correct and normalize skin color in systems' that are sensitive to skin color changes, for example face detection and recognition.


Estimating Scene and Camera calibration Illuminants in color images

本国際会議論文は，ニューラルネットワークにより，過露光や露光不足の状態の顔画像に対する肌色部分を補正する方法を提案している．特に，肌色と色温度との関係を解析的に調べ，ニューラルネットワークの学習機能により補正している．カラー顔画像に対する計算機シミュレーションにより，本手法の有効性を定量的に検証している．


Skin Color Compensation Under Varying Scene Illumination

Proc. of IASTED International Conference on Artificial Intelligence and Soft Computing

We propose to normalize skin color under varying illumination using a graphical method under any type of scene illumination. Under a given illumination, skin color occupies a well-defined region in certain color systems like YIQ, YCrCb etc and therefore can be easily detected using threshold techniques. When a scene illuminant changes, the skin color changes to a different color, but the skin color can still be detected if the threshold were adjusted. This information can be mapped and used to compensate for skin color changes under any illuminant.


Rotational Invariant Human Faces Detection in Color Images

Proc. 20th IASTED Int. Multi-Conf. on Applied Informatics (AI2002)

The International Association of Science and Technology for Development

This paper presents a skin color filter based detection method that can detect faces in one of the three possible orientations. Since skin color is rotational invariant, we propose the use of a Skin-color Edge Angular Measurement (SEAM) method. This method finds the edges of the skin color and then uses the least square (LS) method to draw straight-line segments along the edges. After pairing the line segments, the angle each of them makes with the horizontal plane is then calculated. Using this calculation, it is possible to determine the direction of orientation of the faces, re-rotate them to a frontal pose and then perform face detection. This filter is fast, with a detection accuracy of 75.8% on our test set.


Detection of Human Faces in Visual Scenes

Proc. of Seventh Australian and New Zealand Intelligent Information Systems Conference

This paper presents a neural network based high-speed system to detect faces in any visual scene. This paper proposes a high-speed and accurate method to search for human faces using a "Knowledge" pruned small sized neural network, and skin colour detection using threshold method with confirmation by a skin colour detection neural network (TSCD). This project is made up of two parts: the face detecting system (FDS) and the TSCD. The FDS that is used to detect faces is made up of a knowledge pruned face locator, a down sampler, and a merger. The TSCD does a high-speed reduction of the face search area to skin regions (and then to face candidates). The TSCD assumes, correctly, that a human face (without wearing any mask or painting) in a visual scene can only be found in a skin colour region. However, all skin regions containing faces must be found otherwise the overall system accuracy goes down.


Improved Searching Speed for Human Face Detection in Visual Scenes using Neural Networks

Proc. of International Conference on Neural Information Processing

本国際会議論文は，カラー画像中から人物顔を高速に検出するための方法を提案している．特に，顔の肌色特徴及び顔の幾何学的特長をニューラルネットワークにより検出し，かつ高速に実現する方法である．本方法は複数顔にも対応可能であり，カラー顔画像に対する計算機シミュレーションにより，その有効性を定量的に検証している．