Domain Adaptation for Drivers Gaze Mapping for Different Drivers and New Environments

International Journal of Advances in Intelligent Informatics,


AI Fitness Coach at Home using Image Processing

The COVID-19 pandemic has forced manypeople to exercise at home, leading to a surge in demandfor effective and safe workout programs. However,traditional online exercise videos are often lacking inreal-time feedback, which can compromise exercise safetyand effectiveness. To address this issue, an AI-basedfitness monitoring system, the AI Fitness Coach, hasbeen developed to provide users with real-time guidanceand advice to improve the quality of at-home exerciseand reduce the risk of injury. The AI Fitness Coachsystem utilizes three key units - a pose recognition unit,a fitness movement analysis unit, and a feedback unit - tomonitor and guide the users exercise routine in real-time.By processing images from a fixed camera, the systemanalyzes the users movements and provides feedbackthrough either video or voice. This technology has beenfound to be highly effective in improving exercise qualityat home, leading to enhanced performance and reducedrisk of injury. This article provides an introduction tothe AI Fitness Coach and explores its effectiveness andlimitations. The methodology and technology presentedin this study can offer new ideas and references for the development of future fitness monitoring systems. Despitethe systems impressive results, it has some limitations,such as its requirements for user posture and movement,which make it unsuitable for certain populations. Futureresearch can focus on improving the systems accuracyand applicability. In summary, the AI Fitness Coach isa promising technology that provides a safer and moreeffective way for people to exercise at home. It notonly meets the growing demand for personalized fitnessguidance during the pandemic but also offers new insightsfor the development of fitness monitoring systems. Byenhancing the quality and safety of at-home exercise,the AI Fitness Coach has the potential to revolutionizethe way people exercise and maintain their health in thefuture

International Journal of Human Movement and Sports Sciences


Classification of Typical Pests and Diseases of Rice Based on the ECA Attention Mechanism

Rice, a staple food crop worldwide, is pivotal in agricultural productivity and publichealth. Automatic classification of typical rice pests and diseases is crucial for optimizing rice yieldand quality in practical production. However, infrequent occurrences of specific pests and diseaseslead to uneven dataset samples and similar early-stage symptoms, posing challenges for effectiveidentification methods. In this study, we employ four image enhancement techniquesflipping,modifying saturation, modifying contrast, and adding blurto balance dataset samples throughoutthe classification process. Simultaneously, we enhance the basic RepVGG model by incorporatingthe ECA attention mechanism within the Block and after the Head, resulting in the proposal of anew classification model, RepVGG_ECA. The model successfully classifies six categories: five types oftypical pests and diseases, along with healthy rice plants, achieving a classification accuracy of 97.06%,outperforming ResNet34, ResNeXt50, Shufflenet V2, and the basic RepVGG by 1.85%, 1.18%, 3.39%, and1.09%, respectively. Furthermore, the ablation study demonstrates that optimal classification resultsare attained by integrating the ECA attention mechanism after the Head and within the Block ofRepVGG. As a result, the classification method presented in this study provides a valuable referencefor identifying typical rice pests and diseases


Double-sided design for reinforcing private training of basketball with an advanced imagery opponent

International Journal of Learning Technologies and Learning Environments


Driving Assistance: Pedestrians and Bicycles Accident Risk Estimation using Onboard Front Camera

International Journal of Intelligent Transportation Systems Research


DETECTION OF DRIVER'S VISUAL DISTRACTION USING DUAL CAMERAS

International Journal of Innovative Computing, Information and Control


Auto-Differentiated Fixed Point Notation on Low-powered Hardware Acceleration


Enhanced Vehicle Classification Using Transfer Learning and a Novel Duplication-based Data Augmentation Technique


Vehicle Detection and Type Classification based on CNN-SVM

本稿では，改良されたAlexNetを用いた実道路環境における車両の検出と車種分類を提案する．この手法は物体認識モデルであるAlexNetのサイズを小さくし，分類速度を高速化した上で， SVMによる特徴量の正規化とSpatial Pyramid Poolingによる画像歪み補正により，車両検出や種別分類の性能を向上させることができた．本手法の有効性を検証する実験によって，高い車両検出と種類分類精度を得られることを示した．

International Journal of Machine Learning and Computing


Multimodal Emotion Recognition using Non-Inertial Loss function

Automatic understanding of human emotion in a wild setting using audiovisual signals is extremely challenging. Latent continuous dimensions can be used to accomplish the analysis of human emotional states, behaviors, and reactions displayed in real-world settings. Moreover, Valence and Arousal combinations constitute well-known and effective representations of emotions.In this paper, a new Non-inertial loss function is proposed to train emotion recognition deep learning models. It is evaluated in wild settings using four types of candidate networks with different pipelines and sequence lengths. It is then compared to the Concordance Correlation Coefficient (CCC) and Mean Squared Error (MSE) losses commonly used for training. To prove its effectiveness on efficiency and stability in continuous or non-continuous input data, experiments were performed using the Aff-Wild dataset. Encouraging results were obtained.


Two-Stage Robust Optimization for the Orienteering Problem with Stochastic Weights

In this paper, the two-stage orienteering problem with stochastic weights is studied, where the first-stage problem is to plan a path under the uncertain environment and the second-stage problem is a recourse action to make sure that the length constraint is satisfied after the uncertainty is realized. First, we explain the recourse model proposed by Evers et al. (2014) and point out that this model is very complex. Then, we introduce a new recourse model which is much simpler with less variables and less constraints. Based on these two recourse models, we introduce two different two-stage robust models for the orienteering problem with stochastic weights. We theoretically prove that the two-stage robust models are equivalent to their corresponding static robust models under the box uncertainty set, which indicates that the two-stage robust models can be solved by using common mathematical programming solvers (e.g., IBM CPLEX optimizer). Furthermore, we prove that the two two-stage robust models are equivalent to each other even though they are based on different recourse models, which indicates that we can use a much simpler model instead of a complex model for practical use. A case study is presented by comparing the two-stage robust models with a one-stage robust model for the orienteering problem with stochastic weights. The numerical results of the comparative studies show the effectiveness and superiority of the proposed two-stage robust models for dealing with the two-stage orienteering problem with stochastic weights.


A New Step toward Mastering Double-Under Skill with Supporting Application of Image Processing

The Journal of Information and Systems in Education


Prediction of the VDT Worker's Headache Using Convolutional Neural Network with Class Activation Mapping

本論文ではVDT作業者の疲労具合を検知するためにカメラ映像から6つの手のポーズを分類する手法を提案する．提案手法では，手のポーズの分類に畳み込みニューラルネットワーク(CNN)をベースとしたディープラーニングを適用している．さらに，顔面上の手のポーズの定位のための分類ネットワークの予測を可視化するためのクラス活性化マップ(CAM)を実装した．有効性を検証する実験で，98.5%の精度で認識できることがわかった．

IEEJ Transactions on Electrical and Electronic Engineering


A learning environment of a systematic and tactical style in field sports --- Basic tactic of an offense team in basketball

集団対戦型フィールドスポーツの戦術適用判断の支援環境 --- バスケットボールのオフェンス基本戦術

Transactions of Information Processing Society of Japan


Ellipse Feature Detection in Rope Skipping for Gross Motor Skill Development

To support novice learners to acquire a new skill, it is important to understand the underlying principles ofthe skill. This means that the support system must analyze the skill and develop methods therein to offer credible support toenable efficient learning. This work employs the data from 8 skilled rope skipping subjects to analyze the rope skipping taskto find features that can be used for support. The processes involved include finding the best rope and hand trajectoryfitting using ellipses. This will enable better speed variation detection and timing that can be used to support new learners.In this work, the rope skill attempted is learning to do the double under jump. A double under jump is defined ascompleting two rope rotations per jump. Experimental results prove that this is an effective method for accurate featuredetection and tracking.

International Journal of Engineering Science and Innovative Technology (IJESIT)


Design of a Guide System for Motor Skill Development Targeting a Repetitive Movement Task


Human Detection and Tracking Using HOG Feature and Particle Filter in Video Surveillance System

監視カメラから得られる映像を用いて，HOG特徴とパーティクルフィルタを用いて人間を抽出し，さらに追跡する手法を提案する．本手法ではHOG特徴を用いて人の領域を抽出し，さらにパーティクルフィルタを用いて人の動きを追跡する．これにより複雑な背景においても，複数の人を同時に検出と追跡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，4つのシーンに適用したところ，約90%の検出精度と約99%の追跡精度が得られることがわかった．

International Journal of Advanced Intelligence (IJAI)


Advanced Safety Vehicle (ASV) Technology Driver Support System Monitor using Three Onboard Camera

本論文では，3台の車載カメラを用いた自動車のドライバーの安全運転を支援する手法を提案した．この手法では，3台の車載カメラのうち前方カメラは歩行者と対向車を，後方カメラでは車線変更の際の後方走行車を，ドライバーカメラでは顔の向きと視線の向きを監視し，それを統合させた情報をドライバーに提示するものである．実際の車載カメラ映像を用いた実験によって，安全運転支援をすることが可能であることがわかった．

Journal of the Institute of Industrial Applications Engineers


Towards Autonomous Driving: Road Surface Signs Recognition using Neural Networks

In recent years, the number of traffic accidents has rapidly increased for many reasons. However, the most common cause is the driver carelessness and inattentiveness to road signs. Therefore, the aim of this paper is to automatically recognize road surface markings and by availing the information to the driver, hope to reduce road accidents. In the proposed method, the captured image is transformed and edges information used to extract the target road area. The road marking candidate is then extracted and recognized using a neural network.

本論文では，車載前方カメラから得られる映像を用いて，道路に書かれた道路標示を認識する手法を提案する．この手法では，連続的に得られる車両の前方映像から，縦方向の投影データ，横方向の投影データ，エッジ勾配のヒストグラムを特徴量として，路面上の道路標示をニューラルネットワークにより認識するものである．本手法の有効性を検証するために，実際にシステムを走行車両に搭載して実験を行なったところ，80%以上の精度で検出することができた．

International Journal of Engineering Research and Technology


Multi Cameras Based Indoors Human Action Recognition Using Fuzzy Rules

In this paper, the recognition of human actions in an indoor work environment using multi cameras is proposed. HOG features learned using AdaBoost and optimized by background differencing are used to detect the human regions, while the overlapping camera views are merged using perspective transformation. Initially, to recognize a stationary or mobile person, the distance between the detected area in successive frames is used. The direction the person is facing is estimated using the width of the detected region. Several fuzzy rules are then applied to recognize the human actions based on the person's height measured from the direction they are facing. In addition, suspicious action is recognized using the presence of an abandoned object and detection duration. In experiments, recognition accuracies achieved for "walking" , "stop", running", "sitting", "desk working" and "falling" actions are 87%, 92% , 46%, 95%, 89%, and 91% respectively.

本論文では，マルチカメラを用いたオフィスや病院，公共施設における人間の行動解析手法を提案した．この手法では， HOG特徴量とAdaBoostを用いることで人領域を抽出し，さらに広い範囲に様々な方向に設置された複数台のカメラから連続的に得られる映像から，歩行，停止，走行，座る，作業，倒れるの各動作を認識することができる．実際のオフィスを模擬した環境での実験を行なったところ，約83%の精度で認識できることがわかった．


Road Traffic Signs Recognition using Genetic Algorithms and Neural Networks

International Journal of Machine Learning and Computing


Detection of Abandoned Luggage and Owner Tracking at Sensitive Public Areas

Abandoned objects in sensitive congested public areas like airports or train stations pose a major security threat. Therefore, in this paper, to solve the problem, we propose a novel method for the detection of abandoned luggage, tracking its owner and extracting any other necessary information using multi-threshold pixel based dynamic background model and Earth Mover's Distance (EMD) signature matching. The public area selected for experiments is a train station. The background model is created by learning the color variance in all pixels by allowing multiple thresholds per pixel. After learning, pruning unnecessary thresholds improves the foreground extraction speed. Blob noise and outliers including shadows are deleted by a binarization method based on the Discriminant Analysis (DA) method. A color signature created using the HSV color space that is fast to process, is matched using the EMD metric to track blobs. Once an abandoned object candidate is found, a slower but more accurate SURF algorithm is used to extract feature points for further tracking. Stationary objects after this phase are considered to be abandoned luggage. To prove the effectiveness of the proposed method, experiments are conducted using the i-Lids dataset (2007 IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS 2007)) achieving a frame-based average accuracy of about 93%.

本論文では，動的背景モデルとEarth Mover's Distanceシグネチャマッチングを用いて，鉄道の駅などで放棄された荷物を検出し，所有者を追跡する手法を提案する．この手法では，影の影響を受けないようDiscriminant Analysis(DA)法による2値化法により対象を検出した上で，SURFアルゴリズムを用いることで放棄した所有者を追跡する．i-Lidsデータセットを用いた実験で，約93%の精度が達成できることが確認できた．

IEEJ Transactions on Electronics, Information and Systems


Automatic Background Updating for Abandoned Object Detection at Train Stations

In video surveillance using overhead cameras, it is very important to capture and regularly updated the background to enable accurate extraction of persons or objects of interest. However, in scenes with a lot of movement and stationary objects, for example a train station, it is not easy to update the background without including such objects. In this work, we investigate several objects features and combine them to maintain a stable background image. The features include the speed of the object, texture, shape, associations between persons and objects, etc. The data used is a subset of the i-Lids dataset that was captured for analyzing video systems. It is captured in a train station using one overhead camera. Each video segment is about three minutes long. Index TermsActive background, abandoned objects, shape matching.

本論文では，オーバーヘッドカメラによるビデオ監視のための背景画像の更新手法を提案した．本手法では，鉄道の駅において，放置物の検知のために，象物体の速度，テクスチャ，形状，人とオブジェクトの間の関連などを特徴量として抽出し，さらにそれらを組み合わせることで安定した背景画像更新を可能としている．ビデオシステムの分析のためにキャプチャされたi-Lidsデータセットを用いて実験を行なったところ，本手法の有効性を確認することができた．


A Simple Interface for Mobile Robot Equipped with Single Camera using Motion Stereo Vision


Monotonous Tasks and Alcohol Consumption effects on the Brain by EEG Analysis using Neural Networks

An analysis of the Electroencephalogram (EEG) signals while performing a monotonous task and drinking alcohol using principal component analysis (PCA), linear discriminant analysis (LDA) for feature extraction and Neural Networks (NNs) for classification is proposed. The EEG is captured while performing a monotonous task that can adversely affect the brain and possibly cause stress. Moreover, we investigate the effects of alcohol on the brain by capturing the data continuously after consumption of equal amounts of alcohol. We hope that our work will shed more light on the relationship between such actions and EEG, and investigate if there is any relation between the tasks and mental stress. EEG signals offers a rare look at brain activity, while, monotonous activities are well known to cause irritation which may contribute to mental stress. We apply PCA and LDA to characterize the change in each component, extract it and discriminate using a NN. After experiments, it was found that PCA and LDA are effective analysis methods in EEG signal analysis.

主成分分析および線形判別分析，ニューラルネットワークを用いた飲酒に関するEEGの解析手法を提案する．本手法では，EEG信号から，主成分分析と線形判別分析を用いることで特徴量を抽出し，ニューラルネットワークを用いて解析するものである．本手法の有効性を検証するための実験を行なったところ，主成分分析と線形判別分析はEEG信号の解析に有効であることが確認された．

International Journal of Computational Intelligence and Applications


Video Surveillance using Facial Features-based Tracking

In this work, we present a real time system for facial features detection and tracking in image sequences. We are interested in developing a human tracking system that can improve human-computer interaction and benefit video surveillance problems by making it invariant to rotation, illumination and subject's movement. However, since human body and face movements are very complicated to detect and track, all available cues that can narrow the search space should be considered. This paper describes a novel strategy for both face tracking and facial feature detection. Face detection is important because it reduces the search space and consequently saves time for further face processing, e.g. recognition or transmission. Moreover, facial feature detection enables face normalization which leads to size invariant face recognition. The proposed face tracker resembles human perception in that, initially, it utilizes motion as the major cue and thereafter, searches for the eyes in the areas likely to contain human faces. The presence of a face is determined using an eye tracker. Eyes are important facial features due to their relatively constant interocular distance. In this work, efficiency improvement focuses on two points: reducing template matching area and speeding up the matching process. Our method initially detects two rough eye candidate regions using a feature based method. All other processes are thereafter performed inside the candidate regions. In addition, we can evaluate the size of eye template according to the size of the regions. Altogether, the proposed method combines the accuracy of template based methods and the efficiency of feature based methods in the visual spectrum. To prove the effectiveness of this approach, we performed comparative experiments using real video images. We achieved a real time detection accuracy of about 96%.

動画像の中から顔や手を検出し，追跡する技術は非常に重要である．そこで本稿では，動画像から，顔の検出と手のジェスチャーの検出を高速に行なう手法を提案する．本手法ではまず目を検出する．目は非常に重要な部位であり，また特徴的である．さらにその情報をもとに顔を抽出し，さらに手を抽出する．本手法の有効性を確認する実験を行なったところ，96.3%の精度を得ることができた．

International Journal of Innovative Computing, Information and Control


A Local DCT-II Feature Extraction Approach for personal Identification Based on Palmprint

Biometric applications based on the palmprint have recently attracted increased attention from various researchers. In this paper, a method is presented that differs from the commonly used global statistical and structural techniques by extracting and using local features instead. The middle palm area is extracted after preprocessing for rotation, position and illumination normalization. The segmented region of interest is then divided into blocks of either 8×8 or 16×16 pixels in size. The type-II Discrete Cosine Transform (DCT) is applied to transform the blocks into DCT space. A subset of coefficients that encode the low to medium frequency components is selected using the JPEG-style zigzag scanning method. Features from each block are subsequently concatenated into a compact feature vector and used in palmprint verification experiments with palmprints from the PolyU Palmprint Database. Results indicate that this approach achieves better results than many conventional transform-based methods, with an excellent recognition accuracy above 99% and an Equal Error Rate (EER) of less than 1.2% in palmprint verification.

掌紋に基づくバイオメトリクスの応用は多くの研究者を引きつけてきた．本論文では，大域的な特徴ではなく，局所的な特徴を用いる方法を提案している．本手法の掌の画像に対して回転，位置，輝度の正規化を行い，掌紋の中心部分を抽出する．切り出した領域は，8x8，16x16ピクセルのサイズに変更される．そして，タイプIIの離散コサイン変換(DCT)を適用し，DCT空間に変換する．底中周波数成分を符号化する係数の部分集合が，JPEGスタイルのジグザグ法で選択される．各ブロックの特徴がコンパクトな特徴ベクトルに集約されて，掌紋認証実験が行われる．本手法の有効性を評価するために，PolyU掌紋データベースに対する計算機実験が行われ，99%の精度と1.2%のEqual Error Rate (EER)を達成した．


Vowel Recognition Using Akamatsu Integral and Differential Transforms

International Journal of Advanced Intelligence (IJAI)


Development of a Block based Palmprint Recognition Methodology Using the Discrete Cosine Transform

Hand-based biometric applications based on the palmprint have recently become a popular choice due to their high acceptance by the general public. In this paper, a method is presented which applies the type-II Discrete Cosine Transform (DCT) in a novel way by extracting selected coefficients from small overlapping blocks of scanned, medium-resolution palmprint images to form a feature vector for personal identification. The middle palm area is extracted after rotation, position and illumination normalization during the preprocessing stage before dividing it into 8×8 pixel blocks. The type-II DCT is then used to transform the blocks, and only a small set of the coefficients selected from each block after ranking based on magnitude only. The selected coefficients are concatenated to create a compact feature vector that represents each palmprint. Experiments using a subset of the PolyU Palmprint Database show that this block-based approach effectively discriminates between palmprints, with excellent palmprint recognition results and an Equal Error Rate (EER) of less than 2% in palmprint authentication when using a 64×64 pixel input image block.

掌紋に基づくバイオメトリクス応用は近年，一般的な使用に関して受け入れられつつある．本論文では，個人認証のための特徴ベクトルを構成する小領域から選択的に特徴を抽出するタイプIIの離散コサイン変換(DCT)を用いる方法を提案する．中庸な掌領域は画像正規化後に抽出される．その後，8x8ピクセル領域に分割され，DCTを適用し，少数の係数を取り出し，コンパクトな特徴ベクトルとする．本手法の有効性を評価するために，PolyU の掌紋データベースを用いて実験を行い，64x64ピクセルの入力画像ブロックに対して，2%以下のEqual Error Rate (EER)という優れた結果を得られた．

Australian Journal of Intelligent Information Processing Systems


Fast Approximate Incremental Learning Algorithm based on of Simple-FLDA

This paper presents an Incremental Simple-FLDA. The Incremental Simple-FLDA is a fast incremental learning algorithm based on Simple-FLDA. 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. We performed computer simulations using UCI datasets, EMG data and face data 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 other methods.

逆行列計算を不要とすることによって計算高速性を実現するSimple-FLDA (Fisher Linear Discriminant analysis)において，漸近的な学習を実現するIncremental Simple-FLDAを開発している．この手法は，固有ベクトルを高速計算することが可能であり，学習データの増加に伴い，効率的な識別(パターン認識)を達成することが出来る．実際に，パターン認識で標準的に用いられるUCIデータ(Iris Data)の他，EMGデータ，顔データ などで，識別精度と計算時間の観点からの有用性を評価している．


Detection and Recognition of Vehicle License Plates Using Template Matching, Genetic Algorithms and Neural Networks


Interactive Interface with Evolutionary Eye Sensing and Physiological Knowledge

The purpose of this study is to develop an interactive interface using eye movement to operate a welfare apparatus, such as a feeding device for an orthopedically-impaired person. A part of the purpose is to eliminate special calibration and re-calibration during the operation. Originalities of this proposed system are as follows: eye sensing with evolutionary processing and interactive operation screen based on some physiological knowledge. The proposed system uses a non-contact type interface by the eye movement. An iris is tracked and eye movement is measured by the evolutionary eye sensing (EES) method. An operation screen is divided into 9 areas, which has visual stimulation using the physiological knowledge. A user can select these areas by eye movement and decide by eye fixation. The effectiveness of the proposed system is evaluated by comparison experiments with 20 subjects. The results indicate that the proposed system is easy to use for first-timer, who can become proficient in operation just after a few exercises.

本論文の目的は，肢体不自由者のために，食事装置のような福祉機器を眼の動作で動かす対話的インタフェースを開発することである．特に，本手法は進化的手法を用いて眼(虹彩)の動作(9個のエリアへの分離)を追跡し，生理学的知識を用いてさらに改善している．提案手法は非接触であり，特別なキャリブレーションを必要としない利点がある．本手法の有効性を検証するために，20名の被験者に対して実験を行い，ほとんど訓練がない状態でも高精度に眼の動作を検出できることが判った．

IEEJ Transactions on Electronics, Information and Systems


A DFT-Based Method of Feature Extraction for Palmprint Recognition

Over the last quarter century, research in biometric systems has developed at a breathtaking pace and what started with the focus on the fingerprint has now expanded to include face, voice, iris, and behavioral characteristics such as gait. Palmprint is one of the most recent additions, and is currently the subject of great research interest due to its inherent uniqueness, stability, user-friendliness and ease of acquisition. This paper describes an effective and procedurally simple method of palmprint feature extraction specifically for palmprint recognition, although verification experiments are also conducted. This method takes advantage of the correspondences that exist between prominent palmprint features or objects in the spatial domain with those in the frequency or Fourier domain. Multi-dimensional feature vectors are formed by extracting a GA-optimized set of points from the 2-D Fourier spectrum of the palmprint images. The feature vectors are then used for palmprint recognition, before and after dimensionality reduction via the Karhunen-Loeve Transform (KLT). Experiments performed using palmprint images from the `PolyU Palmprint Database' indicate that using a compact set of DFT coefficients, combined with KLT and data preprocessing, produces a recognition accuracy of more than 98% and can provide a fast and effective technique for personal identification.

過去20∼30年の間，指紋に代表されるバイオメトリクスの研究が活発に行われてきた．掌紋は，最も最近になり研究されだしたが，その一意性，安定性，使いやすさ等で興味が高くなっている．本論文は，効率的で簡単な掌紋認識のための特徴抽出法を提案している．本手法は周波数特徴に変換してから遺伝的アルゴリズムで特徴を最適化する．その後，KL変換で次元圧縮している．本手法の有効性をPolyUデータセットで評価し，98%以上の個人認証の認識精度を得た．


Hour-glass Neural Network based Daily Money Flow Estimation for Automatic Teller Machines

Monetary transactions using Automated Teller Machines (ATMs) have become a normal part of our daily lives. At ATMs, one can withdraw, send or debit money and even update passbooks among other many possi- ble functions. ATMs are turning the banking sector into a ubiquitous service. However, while the advantages for the ATM users (Financial institution customers) are many, the Financial institution side faces an uphill task in management and maintaining the cash flow in the ATMs. On one hand, too much money in a rarely used ATM is wasteful, while on the other, insuficient amounts would adversely affect the customers and may result in a lost business opportunity for the financial institution. Therefore, in this paper, we propose a daily cash flow estimation system using neural networks that enables better daily forecasting of the money required at the ATMs. The neural network used in this work is a fiveve layered hour glass shaped structure that achieves fast learning, even for the time series data for which seasonality and trend feature extraction is difficult. Feature extraction is carried out using the Akamatsu Integral and Differential transforms. This work achieves an average estimation accuracy of 92.6%.


Image Morphing and Warping: Application to Speech Simulation using a Single Image

Journal of Advanced Computational Intelligence and Intelligent Informatics


Fast Incremental Algorithm of Simple Principal Component Analysis

This paper presents a new algorithm for incremental learning, which is named Incremental Simple-PCA. This algorithm adds 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 is possible to update the eigenvector faster by using incremental data. We carry out computer simulations on personal authentication that uses face images and wrist motion recognition that uses wrist EMG by incremental learning to verify the effectiveness of this algorithm. These results were compared with the results of Incremental PCA that introduced incremental learning function to the conventional PCA.

本論文では，Simple-PCA(主成分分析に近似法)を用いる追加学習法を提案している．Simple-PCAは主成分分析を行う最速の近似的方法であり，簡単な繰り返し演算で，固有ベクトルが計算できる．提案手法では，主成分分析の近似学習で，追加されたデータに対して，簡単な方法で固有ベクトルを修正して新たな固有ベクトルを獲得できる．計算機シミュレーションにより，従来の方法と比較して，その有効性を評価している．


Liver tumor extraction from CT images using genetic algorithm

遺伝的アルゴリズムによるCT画像からの肝腫瘍抽出

本論文では，腹部X線CT画像における医師への診断補助システムの構築である．その第一歩として，1スライスのX線CT画像内の病変可能性が最も高い個所を自動的に抽出する領域分割手法を提案している．形状認識の点から，動的輪郭決定手法としてSnakesやLive-wireといった手法が提案されているが，これらの手法は輪郭が不明瞭でCT値も未知な腫瘍に対して効果的ではない．本論文では，遺伝的アルゴリズムにより腫瘍領域形状を最適化する方法を提案している．計算機実験により，その有効性を評価している．


Fingerprints Images Enhancement Using a Concavity and Convexity method

Biomedical Soft Computing and Human Sciences


Enhancement of Palmprint Images Using an Optimized Hexagonal Multilayer Perceptron Neural Network


Evolutionary Video Processing for Lips Tracking

In this paper, real-time tracking and information acquisition of lip region of a person during speech in active video scenes are addressed. The lip is deformed by speech, and the size and orientation are changed by camera motion. The difficulty is mainly due to change of these appearances of the lip. We use template matching with a genetic algorithm to overcome these problems. A high speed and accurate tracking scheme using Evolutionary Video Processing is proposed. Usually, a genetic algorithm is unsuitable for a tracking accuracy of 91.6% and an average processing time of 26.0 milliseconds per frame are achieved.

本国際会議論文では，動作を伴うビデオシーンで会話中の人の口唇領域オンラインで追跡し，かつパラメータを獲得する方法を提案している．更新形状は発話により変形し，カメラ動作に伴い，サイズと方向も変化する．その困難さは主に更新形状の見た目の変化に依存する．そこで，これらの問題を解決するために，進化的ビデオ処理手法を用いて更新領域を追跡する方法を提案する．本手法では，遺伝的アルゴリズムに基づくテンプレートマッチングを使用し，高速性を達成する．本手法により，26ミリ秒で91.6%の検出精度を達成した．

International Journal of Intelligent Computing in Medical Sciences and Image Processing


Genetic Algorithms based Adaptive Search Area Control for Real Time Multiple Face Detection using Neural Networks


Downsized Evolutionary Video Processing for Lips Tracking and Data Acquisitiong

In this paper, high-speed lips tracking and data acquisition of a talking person in natural scenes are presented. Our approach is based on the Evolutionary Video Processing. This method has a trade-off between accuracy and a processing time. To solve this problem, in this paper, we proposed Evolutionary Video Processing with automatic SD-Control. In our simulations, the effectiveness of the proposed method is verified by a comparison experiment. The proposed method improves the performance, speed and accuracy, from 68.4% to 86.2%. Furthermore, it is evaluated that our proposed method can continue to chase the lips region even in such a case. It is demonstrated that the lips region detection and tracking at high speed and with high accuracy is possible, with acquisition of its numerical geometric change information.

本論文では，一般のシーンで，高速高精度に口唇領域を追跡し，口唇形状の有するパラメータを獲得できる方法を提案している．本手法は進化的ビデオ処理に基づいている．また，遺伝的アルゴリズムの探索領域を自動制御する方法も提案している．提案手法では，従来手法の精度を改善しており，86.2%の精度を獲得している．計算機シミュレーションにより定量的に評価し，その有効性と口唇パラメータの獲得が可能であることを示している．


High Speed Genetic Lips Detection by Dynamic Search Domain Control

In this paper, high-speed size and orientation invariant lips detection of a talking person 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 lips. The information is very important for many applications, where high performance is required, such as audio-visual speech recognition, speaker identification systems, robot perception and personal mobile devices interfaces. The difficulty in lips detection is mainly due to deformations and geometric changes of the lips during speech and the active scene by free camera motion. In order to enhance the performance in speed and accuracy, initially, the performance is improved on a single still image, that is, the base of video processing. Our proposed system is based on template matching using genetic algorithms (GA). Only one template is prepared per experiment. The template is the closed mouth of a subject, because the application is for personal devices. In our previous study, the main problem was trade-off between search accuracy and search speed. To overcome this problem, we use two methods: scaling window and dynamic search domain control (SD-Control). We therefore focus on the population size of the GA, because it has a direct effect on search accuracy and speed. The effectiveness of the proposed system is demonstrated by performing computer simulations. We achieved a lips detection accuracy of 91.33% at an average processing time of 33.70 milliseconds per frame.

本論文では，高速で向きとサイズに依存しない口唇領域抽出方法を提案している．特に遺伝的アルゴリズムを用いるテンプレートマッチングによりシステムを構築している．また，口唇領域を構成する位置，サイズ，向きのパラメータは重要であり，これらも抽出できることを示している．また，発話中の口唇領域の変形に対しても対応でき，モバイルフォン等のインタフェースとしても活用できる．特に本手法ではただ一つのテンプレートだけ使用しており汎用性にも優れている．また，遺伝的アルゴリズムの性能を改善するために二つの方法を提案している．本手法の有効性を定量的に評価し，33.7ミリ秒で91.33%の精度を得ていることを示している．

IEEJ Transactions on Electronics, Information and Systems


Feature Generation Method by Geometrical Interpretation of Fisher Linear Discriminant Analysis

This paper presents a new algorithm for feature generation, which is derived based on geometrical interpretation of the fisher linear discriminant analysis (FLDA). This algorithm (Simple-FLDA) is an approximation algorithm that calculates eigenvectors sequentially by an easy iterative calculation by expressing the maximization of variance between classes and minimization of variance in each class without the use of matrix calculation. We carry out computer simulations about recognition of wrist motion patterns by EMG measured from wrist and personal authentications that use face images to verify the effectiveness of this technique. The result was compared with the result of principal component analysis (Simple-PCA).

本論文では，判別分析の幾何学的解釈に基づいて，フィッシャー判別分析の近似的学習アルゴリズムを提案している．本提案方法では通常の判別分析と異なり，行列演算は不要であり，簡単な繰り返しアルゴリズムで判別分析の近似的固有ベクトルを計算することが可能である．本提案手法の有効性を示すために，手首で計測されたEMG(筋電)に対する認識実験と顔認識の実験を行い，Simple-PCA(主成分分析の近似法)との比較において，その有効性を定量的に評価している．


Using Genetic Algorithm for Eye Detection and Tracking in Video Sequence

We propose a high-speed size and orientation invariant eye tracking method, which can acquire numerical parameters to represent the size and orientation of the eye. In this paper, we discuss that high tolerance in human head movement and real-time processing that are needed for many applications, such as eye gaze tracking. The generality of the method is also important. We use template matching with genetic algorithm, in order to overcome these problems. A high speed and accuracy tracking scheme using Evolutionary Video Processing for eye detection and tracking is proposed. Usually, a genetic algorithm is unsuitable for a realtime processing, however, we achieved real-time processing. The generality of this proposed method is provided by the artificial iris template used. In our simulations, an eye tracking accuracy is 97.9% and, an average processing time of 28 milliseconds per frame.

本論文では，サイズと向きに不変な眼の追跡システムを提案し，さらにサイズと向きを表現しているパラメータを獲得できることを示している．本手法では，人間の頭部の動きに柔軟に対応でき，かつ実時間で処理が可能ある．本手法では遺伝的アルゴリズムを用いるテンプレートマッチングを使用している．進化的ビデオ処理により，高速高精度な眼の追跡システムを構築できている．本手法の有効性を定量的に評価し，28ミリ秒で97.9%の検出精度を得ている．

Journal of Systemics, Cybernetics and Informatics


Automatic Human Faces Morphing Using Genetic Algorithms Based Control Points Selection

International Journal of Innovative Computing, Information and Control


Genetic Eye Detection Using Artificial Template


Optimizing Feature Extraction for the Camera Mouse using Genetic Algorithms


Genetic Algorithms Based On-line Size and Rotation Invariant Face Detection


Face Recognition in Color Images Using Neural Networks and Genetic Algorithms

International Journal of Computational Intelligence and Applications


Morphing Human Faces: Automatic Control Points selection and Color Transition

Trans. on Engineering Computing and Technolog


Skin Color Correction through Illuminant Estimation Using Neural Networks and Analytical Methods

本論文は，カラー画像中の顔(肌色部分)を対象として．過露光や露光不足などの光温度が極端に異なる場合の肌色の補正方法を提案している．顔領域を正確に抽出する場合には，顔の肌色範囲を正確に指定することが重要であり，その前処理として肌色部分の色補正を行う方法を提案している．特にニューラルネットワークの学習と円柱座標系での距離計算に基づく補正方法により，高精度に肌色情報を補正し，顔領域を検出できることを計算機シミュレーションにより定量的に検証した．


Human Face Detection in Visual Scenes Using Neural Networks

本論文は，カラー画像中から人間の顔を検出するための高速·高精度な方法を提案している．特に，顔の特徴である肌色情報をニューラルネットワークにより学習検出し，後処理の顔判定を行う領域を低減させ高速化を達成している．後処理では顔の幾何学的特徴を別のニューラルネットワークで学習し，かつ構造化学習によりニューラルネットワークの構造を最小化し，高精度と高速を同時に失言している．カラー画像での計算シミュレーションにより，複数顔が含まれる場合でも高精度に顔検出できることを示した．

IEEJ Transactions on Electronics, Information and Systems


Supporting Stabilization of Timing Difference between Coordinated Motor Actions in Double-under by Sensing Multiple Body Parts

二重跳びにおける身体部位間の運動タイミング差の安定化支援