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.


Classification of Fingerprint Images into Individual Classes Using Neural Networks

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


Facial Gesture Simulation for a Single Image using Image Warping


Multiple Faces Detection in Real Time using Neural Networks

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


A Proposal of Adaptive Graininess Suppression Method

Previous studies of image restoration for noise image were based on mask processing. These conventional noise removal methods represented from mask processing have issue of definition degradation to accompany spacial processing. In this paper, we propose a graininess suppression method based on edge shape. In this method, we detect edges from a noise image and perform graininess suppression for this image based on edge information. On the edge detection, we execute an image transformation for an image that enables us to extract edge by making principal component image. Moreover, we use the canny edge detection operator that can detect a weak edge that relates to a real edge, and do not detect a lie edge. In the suppression process, we use Wiener filter that can restore an noise image without making a complete edge map and the original signal map. We have that the present method for the noise added images to verify effectiveness and have confirmed this.


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.


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.


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.


Optimizing Feature Extraction for the Camera Mouse using Genetic Algorithms

World Scientific and Engineering Academy and Society (WSEAS)


A Method of Gender and Age Estimation Based on Facial Knowledge

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

The purpose of this paper is to propose a method of gender and age estimation which is robust for environmental changing. We propose a feature-point detection method which is the Advanced Retinal Sampling Method (ARSM), and a feature extraction method. As features for the gender and age estimation, facial shape, skin texture, hue and Gabor-feature are used. We examined the left-right symmetric property of the face concerning gender and age estimation by the proposed method.


Graininess Suppression Method for Image Restoration Based on Saving Edge Shape

Previous studies of image restoration for noise image were based on a mask processing. These conventional noise removal methods expressed from the mask processing have an issue of defining degradation to accompany spacial processing. In this paper, we propose a graininess suppression method based on edge shape. In this method, we detect edges from an noisy image and perform graininess suppression for this image based on edge information. On the edge detection, we execute an image transformation for an image that enables us to extract edge by making a principal component image. Moreover, we use the canny edge detection operator, which can detect a weak edge that relates to a real edge, and do not detect a lie edge. In the suppression process, we use the Wiener filter that can restore an noisy image without making a complete edge map and the original signal map. We demonstrated the effectivness of the present method for the noise added image by means of computer simulations.


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.


License Plate Localization Using Template Matching


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


Automatic Extraction System of a Kidney Region Based on the Q-Learning

Knowledge-Based Intelligent Infomation and Engineering Systems

In this paper, a kidney region is extracted as a preprocessing of kidney disease detection. 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 with Neural Network (NN). Furthermore the kidney contour is corrected by using the snakes more accurately. It is demonstrated that the proposed method can detect stably the kidney contour from CT images of any patients.


Prediction of Foul Ball Falling Spot in a Base Ball Game


Influence of Music Listening on the Cerebral Activity by Analyzing EEG


Classification of Electroencephalogram in Listening to the Music by Multivariate Analysis


The EEG Feature Extraction Using the Principal Component Analysis


Automatic Extraction of a Kidney Contour by Narrowing Region Based on the Q-Learning

At present, owing to an aging population and Western-style food, the number of kidney disease patients in Japan is increasing. It is difficult for people to recover fully from kidney disease. Early detection of a kidney disease is therefore needed. But diagnosis based on CT images has faults that are time-consuming and a great labor is required since the quantity of CT images is huge. We propose a method that automatically extracts the kidney region as a preprocessing of kidney failure detection. The kidney region is detected based on contour information that is extracted from the CT image using a dynamic gray scale value refinement method by Q-learning. It is demonstrated that the proposed method can stably detect the kidney from CT images of any patients.


Face Gesture Recognition Using Artificial Neural Networks

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


A Simple Algorithm of Pitch Detection by using Fast Direct Transform

Proceedings 2005 IEEE International Symposium on Computational Intelligence in Robotics and Automation

There are some fundamental frequency detection methods for speech processing such as cepstrum analysis. However, the traditional methods need a lot of computing time and arithmetic processing. Moreover, a speech signal is converted into frequency domain by using an analysis section. In this paper, we propose a fast direct transformation (FDT). FDT extracts an amplitude feature of a signal by a simple computation. We perform the fundamental frequency detection of speech signal by using FDT. We compare the FDT algorithm with the conventional fundamental frequency detection methods by using teacher data (fundamental frequency) detected by the inspection. We perform an improvement as compared with an autocorrelation method by using FDT algorithm.


Proc. of the First International Conference on Complex Medical Engineering

We propose a recognition system based on wrist movements by focusing on ElectroMyoGram (EMG), that is, the living body signal gener-ated with voluntary movement of subject muscles as the initial stage for construction of the total operation device. In this pa-per, 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 com-pression, and Neural Networks (NN) for recognition. Further-more, we reduce the noise using Wavelet Transform for practi-cal application. From results of computer simulation, it is shown that our approach is effective for improvement in rec-ognition accuracy and speed.


Measurement of Skin Texture Using Genetic Programming and Neural Network

In this paper, the purpose of this experiment is to offer a different diagnostic method using sophisticated skin diagnostic system which a layman can easily use at home. A binarization method of images for presumption of skin age is proposed using the genetic programming (GP) which is an evolutional computing method. Skin age can be estimated by skin texture, but it is difficult to binarize skin images owing to individual differences. Therefore, results obtained by a genetic image processing were achieved with consideration of individual differences, and good results were obtained. Furthermore another experiment was carried out using neural network (NN). The fast fourier transform (FFT) was performed to skin images and the classification experiment was carried out by a neural network. Then we began to use newly Simple Principal Component Analysis (SPCA) in order for PCA to achive high speed processing. However it is demonstrated that an effective classification result was


Automatic Detection of Left Ventricular Asynergy by Fuzzy Reasoning

As Japanese daily life is Americanized, heart diseases such as angina and myocardial infarction are increasing. We need to observe consecutive cardiac muscle motion to detect their diseases. In this paper the left ventricular axis and the contact points in the heart region are defined, and then cardiac muscle momentum is extracted. We discriminate an abnormal case and a normal case by using a neural network and fuzzy reasoning to confirm the effectiveness of our approach. Finally, in order to show the effectiveness of the proposed method, we show the simulation examples by using real images.


Automatic Extraction of a Kidney Region by Using the Q-learning

In this paper, a kidney region is extracted as a preprocessing of kidney disease detection. 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 with Neural Network (NN). Furthermore the kidney contour is corrected by using the snakes more accurately. It is demonstrated that the proposed method can detect stably the kidney contour from CT images of any patients.


Discovery of Influence in Music Listening by Analysing EEG

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. In this paper, we propose 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.


Feature Extraction of EEG Patterns Using the Principal Component Analysis and Neural Networks

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

In this paper, a system automatically selects the suitable music for music therapy from a large database of music by each EEG. By using this system, music therapy is done regardless of time and place, and it is needed for constructing this system to clear relations between the music and the EEG. In this paper, we measure the EEG of subjects in listening to music and extract some features of their patterns by using the Principal Component Analysis (PCA). Then we analyze them by using the neural networks (NN). Finally, in order to demonstrate the effective of the proposed method, we carry out the computer simulation. Then, we show the effectiveness of the proposed method.


Face Detection: Size and Rotation Invariance using Genetic Algorithms

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．


A Pitch Detection for Speech Processing by Using a Fast Direct Transformation

There are some pitch detection methods for speech processing such as cepstrum analysis. However, the traditional methods need a lot of computing time and arithmetic processing. Moreover, a speech signal is converted into frequency domain by using an analysis section. In this paper, we propose a fast direct transformation (FDT). FDT extracts an amplitude feature of a signal by a simple computation. We perform the pitch detection of speech signal by using FDT. We compare the FDT algorithm with the conventional pitch detection methods by using teacher data(pitch) detected by the inspection. It is shown that the improvement rate in extraction of fundamental frequency is 21.3%.


Three-Dimensional Geometric Information Acquisition of Lips for Natural Scene

We describe a lips region detection system of a talking person in natural scenes. In particular, we try to acquire numerical parameters to represent the lips information, during speech. Therefore, this lips region extraction must be robust for some considerable geometric changes, as well as varying lips shapes by speech. The present method is image template matching with genetic algorithms (GAs) and projective geometry. We use only one camera. Input images are a template and a target image. The number of template images is only one and it is prepared for each subject. The target image contains the face and the background.


Estimation of Face Direction Using Near-Infrared Camera

This paper presents a method for human face detection and estimating its direction. In particular, the present method tries to extract a face from gray scale images obtained using a near-infrared camera, which is equipped in the top of the automobile windshield. This method is based on template matching (SSDA method) with multi templates and a neural network (NN). After the template matching, NN tries to recognize a face and its direction. Furthermore, the simple PCA is used to yield features suitable for face detection. From results of computer simulations, it is shown that our approach can give a good result compared to another approach.


Real-Time Genetic Lips Region Detection and Tracking in Natural Video Scenes

Proc. of IEEE Conference on Cybernetics and Intelligent Systems'2004

In this paper, real-time detection and tracking of lips region of a talking person in natural scenes is addressed. In particular, we try to acquire numerical parameters to represent the lips information. Because, this information is very important for many applications, such as audio-visual speech recognition, robot perception, and interface of mobile devices. The difficulty lies in deformations and geometric change of lips, by speech and free camera work. Our proposed system is based on template matching with genetic algorithms (GAs). In our previous system, there is a trade-off between accuracy and a processing time. However, we can overcome this by two new methods: (a) a flexible control of a search domain, (b) inheritance of genetic information between video frames. We demonstrated the effectiveness of our proposed system by using some 5 seconds video sequences. The average results are that the accuracy is 94,44% and the processing time is 4.50 seconds.


Automatic Extraction of a Kidney Region by Using the Q-learning

Proc. of International Symposium on Intelligent Signal Processing and communication Systems'2004

At present, owing to an aging population and Western-style food, the number of kidney disease patients in Japan is increasing. It is difficult for people to recover fully from kidney disease. Early detection of a kidney disease is therefore needed. But diagnosis based on CT images has faults that are time-consuming and a great labor is required since the quantity of CT images is huge. We propose a method that automatically extracts the kidney region as a preprocessing of kidney failure detection. The kidney region is detected based on contour information that is extracted from the CT image using a dynamic gray scale value refinement method by Q-learning. It is demonstrated that the proposed method can stably detect the kidney from CT images of any patients.


Measurement of Skin texture Using Genetic Image Analysis

A binarization method of images for presumption of skin age is proposed using genetic programming (GP) which is an evolutionary computing method. Skin age can be estimated by skin texture, but it is difficult to binarize skin images owing to individual differences. Therefore, results obtained by genetic image processing were achieved with consideration of individual differences, and good results were obtained. The fast Fourier transform (FFT) was performed on skin images and a classification experiment was carried out by a neural network (NN) as a comparative experiment. However it is demonstrated that an effective classification result was not obtained by FFT and NN compared to our approach.


Automatic Detection of Left Ventricular Asynergy by Fuzzy Reasoning

Recently, as sophisticated medical instruments have been developed, the internal state of a human body has become well-known. However, a doctor's burden becomes heavier because the number of images which are taken per person with medical instruments drastically increases. Therefore, the development of automatic diagnostic imaging systems is needed. Incidentally, as Japanese daily life is Americanized, heart diseases, such as angina and myocardial infarction, are increasing. We need to observe consecutive cardiac muscle motion to detect their diseases. The left ventricular axis and the contact points in the heart region are defined, and then cardiac muscle momentum is extracted. We discriminate an abnormal case and a normal case by using a neural network and fuzzy reasoning to confirm the effectiveness of our approach. Finally, in order to show the effectiveness of the proposed method, we show simulation examples using real images.


Feature Extraction from EEG Patterns in Music Listening

Various illnesses are caused by stress, and stress release is being carried out by musical therapy. A variety of music is used in the musical therapy, and it takes a long time for patient and music therapist to select the music. Generally, time selecting music can be reduced and the musical therapy can be done more easily if effective music for the purpose it is easily found. For this purpose, we measure and extract an EEG (electroencephalogram) difference between music genres as characteristic data in this paper. Our method produces data based on frequency appearance rate, extracts features by principal component analysis, and then analyzes them by using a neural network. Finally in order to show the effectiveness of the proposed method, we carried out computer simulations by using the real data.


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．


Wrist EMG Pattern Recognition System by Neural Networks and Multiple Principal Component Analysis

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

In this paper, we aim for construction of high-speed and high-accurate system using Fast Fourier Transform (FFT) for feature extraction, Simple-PCA (SPCA) for feature compression, and a neural network (NN) for recognition. In particular, we present a novel method based on Multiple PCA to improve recognition accuracy for EMG. From results of computer simulation, it is shown that our approach is effective for improvement in recognition accuracy and speed.


Object Extraction System by Using the Evolutionaly Computations

In this paper, we propose a new robust thresholds determination method in the various background by using the real coded genetic algorithm (RGA). By using RGA, the most likely plate colors are decided under various light conditions. First, the average brightness Y values of images are calculated. Next, relationship between the Y value and the most likely plate color thresholds (upper and lower bounds) are obtained by GA to estimate threshold equations by using the recursive least squares (RLS) algorithm. Finally, in order to show the effectiveness of the pro-posed method, we show simulation examples by using real images, and result rate of detection is 85.0%.


Face Search by Neural Network Based Skin Color Threshold Method

In recent years, face recognition is becoming important in security or picture search. Many researches on face recognition are premised on acquisition of face position, and face search is therefore important as a preprocessing of face recognition. Although there is a technique of performing face search using skin color information in color images, threshold determination for extracting skin color area is difficult to be done by brightness variance. In this paper, a neural network (NN) is used for this purpose. The thresholds suitable for extracting a skin color area are learned by NN, and face search insensitive to variation in various pictures with a different skin color is performed.


In this paper, we propose a new system identification method by using a genetic algorithm (GA) which has a hybrid structure. The hybrid structure means that a GA has 2 structures. One is the most popular chromosome type GA. That is, chromosomes have binary type genes. The other one is real coded GA. The former is used for determining a function type automatically. The latter is used for determining the coefficient of the function, time delay in the system and combination of the functions automatically. Finally, in order to show the effectiveness of the proposed method, computer simulations were done. Furthermore, in the computer simulations, 2-kinds of systems are identified. One is the hammer stain model. The other is a complex model. From these simulation results, the effectiveness of the proposed method is cleared.


A Feature Extraction of EEG with Individual Characteristics

Proc. of Knowledge-Based Intelligent Information & Engineering Systems'2004

Recently in the world, the research of the electroencephalogram (EEG) interface is done, because it has the possibility to realize an interface that can be operated without special knowledge and technology by using the EEG as a means of the interface. As one of the EEG interface, as for a goal for the final of this research, the EEG control system by any music is constructed. However, the EEG control by music is very difficult because it does not know the music and the causal relation of the EEG clearly. Therefore, the EEG analysis and music analysis is absolutely imperative in this system. In this paper, the EEG analysis method by using the FA and the NN is proposed. The FA is used for extracting the characteristics data of the EEG. The NN is used for estimating extracted the characteristics data of the EEG. Finally, in order to show the effectiveness of the proposed method, classifying the EEG pattern is done computer simulations. The EEG pattern is 4 conditions, which are listening to Rock music, Schmaltzy Japanese ballad music, Healing music, and Classical music.


Genetic Lips Extraction Method with Flexible Search Domain Control

In this paper, a lips extraction method by genetic algorithms (GAs), which has simple, high speed and high accuracy properties is described. This method is a visual front end of audio-visual speech recognition systems for mobile devices. Our method is based on template matching with GA. GA can use a significant amount of computation time, because the calculation and test of fitness values for generation × population is necessary. Furthermore GA is basically a global optimization algorithm. So, premature convergence in GA often causes it to fail to reach the global optimum. To extract lips region at high speed with high accuracy, the population must be small and local search must be more efficient. In order to overcome these problems, we propose the method to control search domain flexibly. We compare this method with the previous method by small population. The experimental results show the proposed method is effective.


Vowel Recognition Method by Using Features Included in Amplitude for Mobile Device

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

We propose a method of individual adaptive vowel recognition system, which uses features extracted from an amplitude waveform of vowel. We think that a simple vowel recognition system can be constructed by using a shape feature of waveform without converting a frequency. Then, we extract the amplitude features and divide five kinds of vowels into each combination of two kinds of vowels. The suitable amplitude features for vowel identification are selected for each vowel combination. A distribution of the suitable amplitude features is indicated by using discrete Voronoi diagram and sphere of influence of the amplitude feature is decided. A borderline between spheres of influence is identified to a function of vowel identification by using the least squares method. Finally, in order to show the effectiveness of the proposed method, computer simulations of vowel recognition are carried out by using the functions of vowel identification.


Medical Diagnosis System Using the Intelligent Fuzzy Systems

We propose the method to develope a system to detect the asynergy in the left ventricle. Processing images are X-ray photograms of the left ventricle by cardiac catheterization. In this paper, we propose the detection system of the asynergy in the left ventricle by using neural networks and the fuzzy inference. Furtheremore, in order to show the effectiveness of the proposed method, we show the simulation example by using the real data.


Feature Extraction for Face Detection and Recognition.

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.


Invariant Lips Extraction for Variation of Horizontal Direction

Proc. of the 6th IASTED International Conference on Signal and Information Processing

In this paper, as a new visual front end of audio-visual speech recognition systems, a simple method for lips re gion extraction is proposed. This method has robustness for varying shape and three-dimensional geometric changes by using only one image template. The image data is repre sented by x component (redness) of the Yxy colour space. The system environment is that a camera and human head run around separately for mobile devices. In our previous study, we tried to extract lips region, which has varying shape by speech, and the geometric changes of lips on two dimension. Then, we extend this previous method to three dimension by using projective geometry. In our simula tions, we try to extract lips region from a face image with background, varying shape by speech, geometric changes, and face horizontal direction change. By this simulation, we can obtain high extraction accuracy.


Wrist EMG Pattern Recognition System by Neural Networks and Genetic Algorithms

Proc. of the 6th IASTED International Conference on Intelligent Systems and Control'2004

In this paper, we aim for construction of a high-speed and high-accurate system using Fast Fourier Transform (FFT) for feature extraction, Simple-PCA (SPCA) for feature compression, and a neural network (NN) for recognition. Furthermore, we reduce the number of units in an input layer of NN using a genetic algorithm (GA) for EMG recognition. From results of computer simulation, it is shown that our approach is effective for improvement in recognition accuracy and speed.


Analysis and Recognition of Wrist Motions by Using Multidimensional Directed Information and EMG signal

Proc. of North American Fuzzy Information Processing Society'2004

We propose a recognition system of wrist motions using EMG signals. The objective in this paper is to recognize 7 wrist patterns using EMG signals measured in wrist. Many EMG signal researches have been presented in humanitarian use for handicapped persons. However, almost all the researches were based on measurement from comparatively large muscular region such as arm and shoulder in data measurement location. The EMG signals are measured with 4-channel sensor in wrist and are recorded as 4 channel's time series data. Multi-dimensional directed information (MDI) are applied to the 4 channel's frequency components. The information capacity, which is the amount of information propagation to flow out of a specific signal to other ones, is computed. The information flow is then used as the feature of wrist motions in a preprocessing, followed by a neural network and motion recognition by the EMG signal is carried out. Finally, in order to demonstrate the effectiveness of the proposed method, computer simulations are carried out for the wrist motions recognition.


Intelligent Medical Diagnosis System Using the Fuzzy and Neural Networks

Various imaging diagnostic technologies are studied and used in practical. It is necessary to develop the automatic diagnosing processing system for detecting the internal organ. In Japan, cardiac disease is one of the most common cause of death. Therefore, it is necessary to measure cardiac function quantitatively and evaluate the motions of continuous cardiac muscle. Moreover, we propose the developing the system to detect the asynergy in the left ventricle. The processing images are X-ray photograms of the left ventricle by cardiac catheterization. In this paper, we propose the detection system of the asynergy in the left ventricle by using neural networks and the fuzzy inference. Furthermore, in order to show the effectiveness of the proposed method, we show the simulation example by using the real data.


License plate detection system by using threshold function and improved template matching method

License plate recognition is very important in an automobile society. However, it is very difficult to do it, because a background and a car surface color can be similar to that of the license plate. Furthermore, detection of cars moving at a very high-speed is difficult to be done. We propose a new method to extract a car license plate automatically by using a genetic algorithm (GA). By using GA, the most likely plate colors are decided under various light conditions. First, the average brightness Y values of images are calculated. Next, relationship between the Y value and the most likely plate color thresholds (upper and lower bounds) are obtained by GA to estimate threshold equations by using the RLS algorithm. Finally, in order to show the effectiveness of the proposed method, we show simulation examples by using real images.


Rule Extraction from a Trained Neural Network for Image Keywords Extraction

This paper presents a rule extraction method from a trained neural network (NN), which is used for keywords extraction from images. In our approach, first, a bit map image in the RGB color space is transformed into that in the L*a*b* color space. Next, it clusters image pixels using the fuzzy c-means method and domains are extracted through a labeling process. Features, such as area of obtained domains, color information, and coordinates of the center of gravity, are then calculated, which are used as input attributes to NN. NN is then trained using such features. After NN learning, rule extraction is carried out using binarized output values in the hidden layer for each keyword. The rules extracted in this paper are If-then rules, which include logical functions. The methods of generating keywords using NN and the rules are presented and their comparative experiments are performed. Finally the validity of these methods was verified by means of computer simulations.


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.


Accuracy and Speed Improvement in Lips Region Extraction

Proc. of Australian and New Zealand Conference on Intelligent Information Systems'2003

In this paper, a method for improvement of extraction accuracy and speed of lips region is described. In our previous study, we tried to extract lips region whose shape is varied by speech. To solve these problem, we use template matching and the matching process is performed using a genetic algorithm. In ddition, we think a characteristic shape and colour of the lips are effective for lips region extraction. In our simulations, we obtain more efficient exploration, high accuracy and very short speed processing time. In this paper, these method of improvement and comparisons of results between our conventional method and improved method are described.


Age Estimation from Face Images by Neural Networks

本国際会議論文は，顔画像から年齢を推定する方法を提案している．特に，顔画像からエッジ検出を行い，この特徴の利用方法を工夫し，かつニューラルネットワークにより学習することにより，高精度に年齢推定を行う方法を提案している．HOIPデータベース300名分に対する年齢推定のシミュレーションにより，従来法よりも簡単な方法で優れた年齢推定精度を得られたことを定量的に検証している．


Rule Extraction from Images Using Neural Networks

In this paper, as a method of extracting keywords, feature data is extracted from an image and a method using a Back-Propagation neural network for learning and classifying them is proposed. In the L*a*b* color space, it clusters image pixels using the c-means method and domains are extracted by Labeling. Features, such as area of the obtained domain, color information, and coordinates of the center of gravity, are then calculated, which are input attributes to a neural network. Moreover, rule generation is carried out by extracting values in the hidden layer of each keyword after the learning of the neural network. Finally the validity of this method was verified by means of computer simulations.


Recognition of Wrist EMG Signal Patterns by Neural Networks

2003 International Symposium on Intelligent Signal Processing and Communication System(ISPACS 2003)


Recognition of Wrist EMG Signal Patterns by Neyral Networks

Proc. of International Symposium on Intelligent Signal Processing and Communication Systems

本国際会議論文は，手首で計測されたEMG信号を識別するためのニューラルネットワークシステムを提案している．特に，手首の7動作を識別するために必要な周波数成分を解析し識別に必要な周波数分布を選択した．また，BP法とLVQ法の認識のための大分類と小分類の組み合わせ方法を提案している．手首で計測されたEMG信号の認識に対する計算機シミュレーションにより，BP&LVQ法の組み合わせが有効であることを定量的に検証している．


A Segmentation Method for Syllable Recognition

本国際会議論文は，音声認識の際に必要な音声波形のセグメント方法を提案している．特に，音声波形の特徴を詳細に解析し，母音と子音の境界を明確化する方法を調査した．そして，その境界を示す特徴を利用して母音部分と子音部分を分離することにより高精度に音素認識ができる方法を提案している．数名の被験者の音素データ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．


A Feature Extraction of the EEG Using the Factor Analysis and the Neocognitron

Proc. of International Conf. on Control Automation and Systems'2003

It is known that an EEG is characterized by the unique and personal characteristics of an individual. Little research has been done to take into account these personal characteristics when analyzing EEG signals. Often the EEG has frequency components which can describe most of the significant characteristics. These combinations are often unique like individual human beings and yet they have an underlying basic characteristic as well. We think that these combinations are the personal characteristics frequency components of the EEG. In this seminar, the EEG analysis method by using the Genetic Algorithms (GA), Factor Analysis (FA), and the Neural Networks (NN) is proposed. The GA is used for selecting the personal characteristic frequency components. The FA is used for extracting the characteristics data of the EEG. The NN is used for estimating the characteristics data of the EEG. Finally, in order to show the effectiveness of the proposed method, classifying the EEG pattern is carried out via computer simulations. The EEG pattern is evaluated under 4 conditions: listening to Rock music, Schmaltzy Japanese ballad music, Healing music, and Classical music. The results, when personal characteristics frequency components are NOT used, gave over 80 % versus 95 % accuracy when personal characteristics frequency components are used. This result of our experiment shows the effectiveness of the proposed method.


The Important Frequency Band Selection and Feature Vector Extraction System by an Evolutional Method

本国際会議論文は，手首で計測されたEMG信号を識別するための進化的EMG認識システムを提案している．特に，手首の7動作を識別するために必要な周波数成分を解析し，識別に必要な周波数分布を選択している．また，選択された特徴の組み合わせに基づく関数に基づく特徴生成を行い精度改善に効果があることを示した．手首EMGの認識に対する計算機シミュレーションにより，その関数生成の有効性を主成分分析と比較して，定量的に検証している．


Number Plate Detection System by Using the Night Images

本国際会議論文は，ナンバープレート検出を効率的に行う際の領域分割のための閾値設定方法を提案している．特に実数値型遺伝的アルゴリズムにより，画像の輝度情報からプレート領域を決定するための上下閾値を効率的に決定し，さらに様々な画像処理手法により精度改善する方法を提案し結果を改善している．実際の自動車画像に対する計算機シミュレーションにより，夜間の暗い画像対してもプレート検出が高速かつ高精度に行えることを検証している．


Color-Identification System Using the Sandglass-Type Neural Networks

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

画像の著作権保護のために，画像自身が持つ特徴を鍵とする，カラーIDを作成する手法を提案した．手法には，砂時計型ニューラルネットワークを用いて，画像情報を圧縮し，カラーIDを作成している．そして提案手法の有効性を検証するために，顔画像を用いて実験を行ない，アプリケーションを開発した．


Tracking of Moving Object Using Deformable Template

A method to track a moving object that has a continuous shape deformations is described in this paper. The deformation of the template and matching process are performed using a genetic algorithm. A new method, which distinguishes between an object function and fitness function, is proposed in this paper. Comparisons between our conventional method and the new method are also simulated. As an object for this simulation, a lips region during speech is used. From results of this simulation, it comes to light that the proposed method is more better than the conventional one in tracking accuracy and speed.


Reconstruction of Facial Skin Color in Color Images

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.


True Smile Recognition Using Neural Networks and Simple PCA

Recently, an eigenface method by using the principal component analysis (PCA) is popular in a filed of facial expressions recognition. In this study, in order to achieve high-speed PCA, the simple principal component analysis (SPCA) is applied to compress the dimensionality of portions that constitute a face. By using Neural Networks (NN), the difference in value of cosθ between true and false (plastic) smiles is clarified and the true smile is discriminated. Finally, in order to show the effectiveness of the proposed face classification method for true or false smile, computer simulations are done with real images.


Recognition of EMG Signal Patterns by Neural Networks

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

This paper tries to recognize EMG signals by using neural networks. The electrodes under the dry state are attached to wrists and then EMG is measured. These EMG signals are classified into seven categories, such as neutral, up and down, right and left, wrist to inside, wrist to outside by using a neural network. The NN learns FFT spectra to classify them. Moreover, we structuralized NN for improvement of the network. It is shown that our approach is effective to classify the EMG signals by means of computer simulations.


A Neural Network Approach to Color Image Classification

Proc. of Knowledge-Based Intelligent Information & Engineering System

This paper presents a method for image classification by neural networks which uses characteristic data extracted from images. In order to extract characteristic data, image pixels are divided by a clustering method on YCrCb 3-dimensionl-color space and processed by labeling to select domains. The information extracted from the domains is characteristic data (color information, position information and area information) of the image. Another characteristic data, which is extracted by Wavelet transform, is added to the feature and a comparative experiment is conducted. Finally the validity of this technique is verified by means of computer simulations.


A Feature Extraction of the EEG Using the Factor Analysis and Neural Networks

In this paper, the EEG analysis method by using the GA, the FA, and the NN is proposed. The GA is used for selecting the personal characteristics frequency compnents. The FA is used for extracting the characteristics data of the EEG. The NN is used for estimating extracted the characteristics data of the EEG. Finally, in order to show the effectiveness of the proposed method, classifying the EEG pattern does computer simulations. The EEG pattern is 4 conditions, which are listening to Rock music, Schmaltzy Japanese ballad music, Healing music, and Classical music. The result, in the case of not using the personal characteristics frequency components, gave over 80 % using the personal characteristics frequency components, gave over 95 % effectiveness of the proposed method.


A Feature Extraction Method for Personal Identification System

Proc. of Knowledge-Based Intelligent Information & Engineering Syst

Recently, personal identification using faces are used because of needless for physical contact. However, when the number of registrant of a system increase, the recognition accuracy of system will get worse certainly. Therefore, in order to improve the recognition accuracy, it is necessary to extract the feature area effectively for getting the high recognition accuracy. In this paper, we analyze and examine about the individual feature in a face using the GA and the SPCA. Thus, by removing the area which is not valuable, we think that recognition accuracy becomes high. Then, in order to show the effectiveness of the proposed method, we show computer simulations by using real image.


Recognition from EMG signals by an Evolutional Method and Non-Negative Matrix Factorization

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

In this paper, we propose a method of the noise rejection from a signal acquired from many channels using the Electromyograph (EMG) signals. The EMG signals is acquired by the 4th electrodes. EMG signals of 4ch(es) is decomposed into two processions using Non-Negative Matrix Factorization(NMF). And noise rejection is performed by applying the filter obtained by GA to the decomposed matrix . After performing noise rejection, EGM signals is reconstructed and the acquired EMG signal is recognized. The EMG signals based on 7 operations at a wrist are measured. We show the effectiveness of this method by means of computer simulations.


License Plate Detection using Hereditary Threshold Determined Method

Proc. of Knowledge-Based Intelligent Information & Engineering Systems'2003, Oxford, England, pp.585-593 (2003).

In this paper, we propose a new thresholds determination method in the various background by using the real-coded genetic algorithm (RGA). By using RGA, the most likely plate colors are decided under various light conditions. First, the average brightness Y values of images are calculated. Next, relationship between the Y value and the most likely plate color thresholds (upper and lower bounds)are obtained by RGA to estimate thresholds function by using the recursive least squares (RLS) algorithm. Finally, in order to show the effectiveness of the proposed method, we show simulation examples by using real images.


The Image Recognition System by Using the FA and SNN

In this paper, keywords in the image are analized by using the factor analysis and the sandglass-type neural network (SNN) for image searching. As images preprocessing, objective images are segmented by the maximin-distance algorithm. Small regions are integrated into a near region. Thus, objective images are segmented into some region. After this images preprocessing, keywords in images are analyzed by using factor analysis and a sandglass-type neural network (SNN) for image searching in this paper. Images data are corresponded to 2-dimensional data by using these two methods. 2-dimensional data are plotted on a graph. Images are recognized by using this graph.


3-Dimensional Object Recognition by Evolutional RBF Network

This paper tries to recognize 3-dimensional objects by using an evolutional RBF network. Our proposed RBF network has the structure of preparing four RBFs for each hidden layer unit, selecting based on the Euclid distance between an input image and RBF. This structure can be invariant to 2-dimensional rotation by 90 degree. The other rotational invariance can be achieved by the RBF network. In hidden layer units, the number of RBFs, form, and arrangement are determined using real-coded GA. Computer simulations show object recognition can be done using such a method.


The Recognition Method of the EEG Feature Pattern Using the Factor Analysis

Proc. of 34th Annual Conference of the International Simulation and Gaming Association

本稿では，聴取曲のジャンルを脳波で分類する手法を提案する．提案手法は，脳波計測部，脳波の特徴抽出部，聴取曲分類部で構成される．脳波は簡易脳波計を用いて計測する．脳波の特徴は，周波数分析された脳波の周波数成分間の相互関係を因子分析により分析し，定量化することで抽出する．聴取曲は抽出された脳波の特徴をニューラルネットワークに学習し，分類する．提案手法の有効性を検証するために，実験を実施した．その結果，90%以上の精度を得るに至った．


Face Extraction and Identification System Using Double Structure Neural Networks

本国際会議論文は，カラー画像中の顔を検出するために2種類のニューラルネットワークを用いる手法を提案している．特に本方法では肌色と唇色を別々に学習認識し，顔検出の精度を改善する方法である．顔判定は肌色中に存在する一定以上の大きさの唇色領域により行っている．認識時間は比較的に長いが，実際のシーン画像に対する計算機シミュレーションにより，顔検出が高精度に行えることを検証している．


Research on Drift Ice Detection from SAR Images Using Bio-Information Processing

Proc. of the 5th IASTED International Conference on Signal and Information Processing

In this paper, drift ice is detected in order to prevent marine accident in winter and improve reliability of sea data. This paper aims at automatic detection of drift ice using NN (Neural Networks) effective in nonlinear processing. Furthermore, GA (Genetic Algorithms) is used for reduction of the network size of NN and acquisition of the data that function effective in drift ice detection.


Improvement of Lips Region Extraction Method for Lipreading

In this paper, a lips extraction method at the mo ment of speech is described. Our method uses template matching and the matching process is performed using a genetic al gorithm. Only one template of closed mouth region is used in consideration for using on mobile devices. Parameters resolved by the genetic algorithm show fine location and geometric change. As an image data, a modified x compo nent (redness) of the Yxy colour space. A new method, which distinguishes between an objective function and fitness function, is proposed in this paper. We simulate comparison between our conventional method and the new method. Experimental results indicate that the new method is more better than the conventional one in extrac tion accuracy and speed.


The EEG Detection System Using the Factor Analysis and Neural Networks

Proc. of 7th World Multi-Conference on Systemics, Cybernetics and Informatics

本国際会議論文は，音楽鑑賞時の脳波の解析を行うことを目的とし，遺伝的アルゴリズムによる特徴選択とニューラルネットワークによる識別を行う方法を提案している．特に必要な周波数成分を遺伝的アルゴリズムにより決定し，因子分析を適用して特徴抽出を行い，さらにニューラルネットワークにより学習することにより鑑賞している音楽を高速に分類する方法を提案している．数名の被験者から計測された脳波信号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．


License Area Extraction System Using the Real-Coded GA

本国際会議論文は，自動車ナンバープレート検出を行うための閾値設定方法を提案している．特に実数値型遺伝的アルゴリズムにより，画像の輝度情報からプレート領域を決定するための上下閾値を効率的に決定し，さらに様々な画像処理手法により精度改善する方法を提案している，実際の自動車画像に対する計算機シミュレーションにより，プレート検出が高速かつ高精度に行えることを検証している．


Feature Vector Extraction System from EMG Signals Using Gentic Algorithm

本国際会議論文は，手首で計測されたEMG(筋肉電位)を用いて手首7動作を識別するための方法を提案している．特に，遺伝的アルゴリズムにより必要な周波数成分を選択し，クラス間分離を大きくする評価関数を導入することにより精度を改善する方法を提案している．実際の手首EMG信号に対する計算機シミュレーションにより，手首動作識別が高精度に行えることを検証している．


A Feature Extraction Method for Personal Identification System by Using Real-Coded Genetic Algorithm

本国際会議論文は，実数値型遺伝的アルゴリズムにより個人認証を高精度に行うための特徴抽出方法を提案している．特に顔領域の重要度を実数値型遺伝的アルゴリズムにより決定し，Simple PCAにより特徴を生成し，さらにニューラルネットワークにより学習認識する方法を提案している．100名の被験者(ただし，学習用の被験者は50名)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．本国際会議において，Best Presentation Award を受賞した．


A Feature Extraction of the EEG during Listening to the Music using the Factor Analysis and Neural Networks

Proc. of International Joint Conference on Neural Network

Recently in the world, the research of the electroencephalogram (EEG) interface is done, because it has the possibility to realize an interface that can be operated without special knowledge and technology by using the EEG as a means of the interface. As one of the EEG interface, as for a goal for the final of this research, the EEG control system by any music is constructed. However, the EEG control by music is very difficult because it does not know the music and the causal relation of the EEG clearly. Therefore, the EEG analysis and music analysis is absolutely imperative in this system. In this paper, the EEG analysis method by using the FA and the NN is proposed. The FA is used for extracting the characteristics data of the EEG. The NN is used for estimating extracted the characteristics data of the EEG. Moreover teacher signal data of the NN uses the data of the characteristics data of the music. The characteristics data of music is extracted by using the Bark scale analysis. Finally, in order to show the effectiveness of the proposed method, classifying the EEG pattern is done computer simulations. The EEG pattern is 4 conditions, which are listening to Rock music, Schmaltzy Japanese ballad music, Healing music, and Classical music.


Recognition system for EMG signals by using non-negative matrix factorization

IN this paper, the feature vector of a few dimensions for the electromyograph (EMG) recognition systems is extracted. We aim at the construction of the comprehensive operation equipment to which the operation used frequently was summarized. Important frequency bands of EMG signals are selected by using a genetic algorithm. The EMG signals are a kind of the living organism signal. The EMG signals based on 7 operations at a wrist are measured and recognized. We perform a recognition experiment of EMG signals by neural network using the selected frequency band. We show the effectiveness of this method by means of computer simulations.


Face detection and emotional extraction system using double structure neural networks

In this paper, we propose a new method to examine whether or not human faces are included in color images by using a lip detection neural network (LDNN) and a skin distinction neural network (SDNN). First, the lip is detected by LDNN in the proposed method. Next, SDNN is utilized to distinguish the skin color from the others. The proposed method can obtain relatively high recognition accuracy, since it has the double recognition structure of LDNN and SDNN. Finally, in order to demonstrate the effectiveness of the proposed scheme, computer simulations were performed.The validity was verified by testing images containing several faces.


A design of the object detection system using the RGA

License plate recognition is very important is an automobile society. However, it is very difficult to do it, because the background and body color of cars are sometimes similar to that of the license plate. Furthermore, the detection of cars moving at a very high speed is difficult. In this paper, we propose a new robust thresholds-determination method in various backgrounds by using the real coded genetic algorithm (RGA). By using RGA, the most likely plate colors are decided under various light conditions. First, the average brightness Y values of images are calculated. Next, relationship between the Y value and the most likely plate color thresholds (upper and lower bounds) are obtained by GA to estimate threshold equations by using recursive least squares (RLS) algorithm. Finally, in order to show the effectiveness of the proposed method, we show simulation examples by using real images. The resulting rate of detection is 85.0%.


An EEG Feature Detection System Using the Neural Networks Based on Genetic Algorithms

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

In this paper, the EEG analysis method by using the GA, the FA and the NN is proposed. The GA is used for selecting the personal characteristics frequency components. The FA is used for extracting the characteristic data of the EEG. The NN is used for estimating extracted the characteristics data of the EEG. Finally, in order to show the effectiveness of the proposed method, EEG pattern was classified using computer simulations. The EEG pattern has 4 conditions, which are listening to rock music, Schmaltzy Japanese ballad music, healing music and classical music. The result, in the case of not using the personal characteristics frequency components, gave over 95% accuracy. This result of our experiment shows the effectiveness of the proposed method.


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

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.


A Feature Extraction Method in Face Image for Personal Identification

Recently in the world, many researches of personal identification method using biometrics are widely done. Especially, personal identification using the face is used because of needless for physical contact. In this paper, we propose the new method to identify the personal using the SPCA and the NN. The SPCA has been proposed in order for the PCS to achieve high-speed processing. Moreover, the personal identification system needs to have not only ability to recognize registrant correctly but also ability to reject un-registrant certainly. Therefore, we perform simulation using un-registrant. Furthermore, we analyzed and examined about the individual feature in a face by using the GA and the SPCA. Then, in order to show the effectiveness of the proposed method, we show computer simulations by suing the real image. From these results, we show the effectiveness.


Thresholds Decision Method for Fast Object Detection Systems

In this paper, we propose a new determination method of color thresholds for extracting objects. In order to obtain the optimal color threshold, it is necessary to determine RGB values all together because one color consists of a combination of RGB values. The two optimal thresholds, an upper and a lower, are determined for object extraction by applying genetic algorithm. Moreover, in the proposed method, individuals are evaluated using two fitness functions for obtaining the optimal color thresholds, and initial population is generated with color histograms for fast convergence. Finally, computer simulations are performed, and the result images are shown.


Genetic Lips Extraction Method for Varying Shape

In this paper, a lips extraction method that can extract lips region from varying lips shape at the moment of speech by using only one template image is described. The method that is proposed in this paper, has invariance for an open and closed mouth, showing or not showing any teeth, and has high speed and high extraction accuracy in consideration for characteristics of the lips by using the genetic method. This method uses the template matching based on the genetic algorithm. Furthermore, the colour of lips and characteristics of the lips shape variances at the moment of speech in this system are utilized. The effectiveness of this method is demonstrated with only one template for each person being tested and a search object, that is, the varying lip shapes at the moment of speech of vowels by means of computer simulations. These computer simulations indicate that this method can extract the varying lips shape at the moment of speech by using only one template. Moreover, in the extraction processing of every vowel, a high speed and high extraction accuracy can be obtained.


License Plate Detection System in Rainy Days

License plate recognition is very important in an automobile society. However, it is very difficult to do it, because a background and a body color of cars are similar to that of the license plate. Furthermore, the detection of cars in a moving at a very high-speed is difficult to be done. In this paper, we propose a new robust thresholds determination method in the various background by using the real coded genetic algorithm (RGA). By using RGA, the most likely plate colors are decided under various light conditions. First, the average brightness Y values of images are calculated. Next, relationship between the Y value and the most likely plate color thresholds (upper and lower bounds) are obtained by GA to estimate threshold equations by using the recursive least squares algorithm. Finally, in order to show the effectiveness of the proposed method, we show simulation examples by using real images.


Robust Face Detection for Direction Changing Using Evolutionary Algorithms

Threshold selection in multi-value images is performed based on their color information. When the threshold in an image is fixed it lacks versatility for the others. Because the color information varies under the influence of light conditions. In this paper, a Genetic Algorithm (GA) is used to select the most likely values of lips and skin colors in a light condition. It is possible to extract objects from the multi-value image only with the color information. In this paper, the objects of extraction are chosen to be the human lips and skin colors.


Feature Analysis for the EMG Signals Based on the Class Distance

In this paper, a feature vector is extracted from an electromyography (EMG) signal at a wrist, and the EMG signals based on 7 motions are recognized. In order to perform good pattern recognition, it is desirable that the distance in feature vector between classes is far, and that the variance in a class is small. In consideration of these, important frequency bands of EMG signals are selected by using a genetic algorithm. We use the selected frequency band to perform the recognition experiment of EMG signal by a neural network. Finally, the effectiveness of this method is demonstrated by means of computer simulations.


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.


A Design of Face Detection System using the GA and the Simple PCA

Recently in the world, many researches of individual recognition method using biometrics are widely done. Especially, individual recognition using faces are used because of needless for physical contact. When we specify with an individual, the information from a face is the most reliable. In this paper, we propose the new method of individual recognition using Simple PCA and NN. Individual recognition system needs to have not only ability to recognize the registrant correctly but also ability to reject the other person certainly. therefore, our objective is to reject certainly the other person. Furthermore, we analyzed and examined about the individual feature in a face using GA. Moreover, in order to show the effectiveness of the proposed method, we show computer simulations by using real image. From these results, we show the effectiveness.


Recognition of EMG Signal Patterns by Neural Networks

This paper tries to recognize EMG signals by using neural networks, These EMG signals are classified into seven categories, such as neutral, up and down. right and left, wrist to inside and wrist to outside. The neural network learns FFT spectra to classify them. It is shown that our approach is effective to classify the EMG signals by means of computer simulations.


A Neural Network Approach to Color Image Classification

The paper presents a method for image classification by neural networks which uses characteristic data extracted from images. In order to extract characteristic data, image pixels are divided by a clustering method on HSI 3-dimensional-color space and processed by labeling to select domains. The information extracted from the domains is characteristic data (color information, position information and area information) of the image. Another characteristic data, which is extracted by Hough transform, is added to the feature and a comparative experiment is conducted. Finally the validity of this technique is verified by means of computer simulation.


True Smile Recognition System using Neural Networks

Recently, research about man-machine interfaces has increased. Therefore application to facial expressions is expected from the development of the man-machine interface. An eigen-face method is popular in these research fields by using the principal component analysis (PCA). But in PCA, it is not easy to compute eigenvectors with a large matrix when considering the cost of calculation to adapt for time-varying processing. In order for PCA to become high-speed, the simple principal component analysis (SPCA) is applied to compress the dimensionality of portions that constitute a face. A value of cos θ is calculated using the eigenvector and the gray-scale image vector of each picture pattern. By using neural networks (NN), the value of cos θ between true and false (plastic) smiles is clarified and the true smile is discriminated. Finally, in order to show the effectiveness of the proposed face classification method for true or false smile, computer simulations are done.


Face Detection and Emotional Extraction System using Double Structure Neural Networks

We propose a new method to examine whether or not human faces are included in color images by using a lip detection neural network (LDNN) and a skin distinction neural network (SDNN). In conventional methods, if there exists the same color as the skin color in scenes, the domain which is accepted as not only the skin color but any other color can be searched. However, first, the lips are detected by LDNN in the proposed method. Next, SDNN is utilized to distinguish skin color from the other colors. The proposed method can obtain relatively high recognition accuracy, since it has the double recognition structure of LDNN and SDNN. Finally, in order to demonstrate the effectiveness of the proposed scheme, computer simulations were performed. First, 100 lip color, 100 skin color and 100 background pictures, which are changed into 10×10 pixels, are prepared for training. The validity was verified by testing images containing several faces.


Drift ICE Recognition using Remote Sensing Data by Neural Networks

In recent years, observation of a wide variety in the Earth's surface can be done by improvement of remote sensing technology. The purpose of the paper is to recognize a drift ice as thick ice, thin ice, and sea using synthetic aperture radar (SAR) images. The recognition of the drift ice is achieved by using neural networks (NN). The neural network applies two methods, a BP trained neural network and a self-organizing map. Training data are image features extracted from SAR images. There are three methods for extracting the features: Fourier transform, high-order autocorrelation function (HACF), and image features based on a run length method. We carry out a comparative experiment, and demonstrate their effectiveness by means of computer simulation.


A proposal of emotional detection system from speech data

Proc. of Knowledge- Based Intelligent Information Engineering Systems & Allied Technologies

本国際会議論文は，ニューラルネットワークにより人間の音声が有する感情を抽出するための方法を提案している．まず，発生音声から音声の基本情報であるピッチを抽出するが，この際に専用のIC回路により精度よくピッチ抽出を行う．そして，その時間的変化をニューラルネットワークにより識別し感情判定を行う．多層ニューラルネットワークにより感情音声の2次元分布を解析するとともに，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．


Key words extraction for image search of WWW

本国際会議論文は，インターネット上での画像検索のためのキーワード抽出の方法を提案している．特に，画像を領域分割し，様々な特徴量を抽出してニューラルネットワークでの学習によりキーワード抽出を行っている．多数の画像を用いた計算機シミュレーションにより，本手法によるキーワード抽出の有効性を定量的に検証している．


Drift ice detection in remote sensing images using neural networks and genetic algorithms

本国際会議論文は，ニューラルネットワークにより，リモートセンシングのSAR(合成開口レーダ)画像から，冬季に北海道オホーツク海側に流れてくる流氷域を高精度に抽出する方法を提案している．特に，画像特徴として，フーリエスペクトル，濃度共起行列，その他の統計量などを抽出し，ニューラルネットワークで学習識別する方法である．従来法よりも高精度にカラーSAR画像から流氷抽出できることを計算機シミュレーションにより検証している．


High-speed face search by threshold value determination method using neural networks

本国際会議論文は，ニューラルネットワークにより，複雑な背景の画像から顔領域を高速かつ高精度に抽出する方法を提案している．特に，ニューラルネットワークで肌色領域の色に関する上下の閾値を学習的に獲得する方法である．本手法により，顔画像に対して肌色領域を直接指定できるため，従来法よりも格段に高速かつ高精度にカラー顔画像から顔抽出できる手法である．様々な加須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．


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．


A system identification method using a hybrid-type genetic algorithm

In this paper, we propose a new system identification method by using a genetic algorithm (GA) which has a hybrid structure. The hybrid structure means that a GA has 2 structures. Finally, in order to show the effectiveness of the proposed method, computer simulations were done. Furthermore, in the computer simulations, 2-kinds of systems are identified. One is the hammer stain model. The other is a complex model. From these simulation results, the effectiveness of the proposed method is cleared.


Lips extraction with template matching by genetic algorithms

本国際会議論文は，遺伝的テンプレートマッチングにより，複雑な背景の顔画像から口唇領域を高精度に抽出する方法を提案している．特に，遺伝的アルゴリズムで染色体の構造とテンプレート部分を従来とは異なった方法に工夫することにより，従来法よりも高速かつ高精度にカラー顔画像から口唇抽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.


A Proposal of Face Decision Standard Using Dual-structured Neural networks

Proc. of Artificial Intelligence and Soft Computing

In this paper, we propose a new method to examine whether or not human faces are included in color images by using a lip detection neural network (LDNN)and a skin distinction neural network (SDNN). First, the lip are detected by LDNN in the proposed method. Next, SDNN is utilized to distinguish the skin color from the others. The proposed method can obtain relatively high recognition accuracy, since it have the double recognition structure of LDNN and SDNN. Finally, in order to demonstrate the effectiveness of the proposed scheme, computer simulations were performed.


Emotional Space Map Generation Method by Using Neural Networks


Feature Data Selection for Rainfall Forecast by Using Real-Coded GA

In this paper, rainfall is forecasted by using a Neural Network (NN) and a Genetic Algorithm (GA). GA selects data needed to predict the rainfall. NN learns and forecasts it using attributes selected by GA. The real-coded GA is used to decide data priority degree, and data really needed for the rainfall forecast is selected based on the priority. Finally, in order to show the effectiveness of the proposed fainfall forecast system, computer simulations are performned for real weather data.


A Proposal of Impression Extraction System Using a Neural Network and a Genetic Algorithm

Proc. of 6th World Multiconference on Systemics, Cybernetics and Informatics

本国際会議論文は，進化的手法によりカラー画像中の人間の顔領域を検出し，表情を識別するための方法を提案している．まず，カラー顔画像の肌色範囲を画像ごとに設定し，その情報に基づいて肌色閾値を表す線形方程式を決定する．この方程式により様々な輝度画像での肌色範囲を表す閾値を高速に精度よく決定でき，かつ表情識別を行えることを計算機シミュレーションにより検証している．


EMG Signal Recognition by Using Neural Networks

Proc. of International Symposium on Advanced Control of Industrial Processes

This paper tries to recognize EMG signals by using neural networks. The electrodes under the dry state are attached to wrists and then EMG is measured. These EMG signals are classified into seven categories, such as neutral, up and down, right and left, wrist to inside, wrist to outside by using a neural network. The NN learns FFT spectra to classify them. Moreover, we structuralized NN for improvement of the network. It is shown that our approach is effective to classify the wrist EMG signals by means of computer simulations


Genetic Fog Occurrence Forecasting System Using a LVQ Network

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

The International Association of Science and Technology for Development

本国際会議論文は，遺伝的手法により霧発生を予測するシステムの構成方法を提案している．特に，実数値型遺伝的アルゴリズムにより霧発生に影響する入力属性を選択し，学習ベクトル量子化法で予想する方法を提案している．進化手法により属性値が重み付けされるため，学習後に各属性の優先度が明確になる特徴を有する．気象庁から戴いた千歳空港のデータによ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．


Fast Face Detection System for Time-Varying Images Using the GA-Threshold Method

Threshold selection in multi-value images is performed based on their color information. When the threshold in an image is xed it lacks versatility for the others. Because the color information varies under the in uence of light conditions. In this paper, a Genetic Algorithm (GA) is used to select the most likely values of lips and skin colors in a light condition. It is possible to extract objects from the multi-value image only with the color information. In this paper, the objects of extraction are chosen to be the human lips and skin colors.


Recognition of Facial Expressions Based on Simple PCA

In this paper, the simple principal component analysis (SPCA) is applied to dimensionality compression of por tions that constitute a face, which is a data-oriented fast method. An angle (cos θ) is calculated using the eigenvec tor and the gray scale image vector of each picture pattern. By using the value of cos θ, similarity between true and false (plastic) smiles is clari ed and the true smile is dis criminated. Finally, in order to demonstrate the effective ness of the proposed face smile or false classifying method, computer simulations are done.


Rainfall Forecast Using a Neural Network with a Real Coded Genetical Preprocessing

In this paper, rainfall is forecasted by a Neural Network (NN) and a Genetic Algorithm (GA). GA selects data needed to predict the rainfall. NN learns and forecasts it using attributes selected by GA. The real-coded GA is used to decide data priority degree, and data really needed for the rainfall forecast is selected based on the priority. Finally, in order to show the effectiveness of the proposed rainfall forecast system, computer simulations are performed for real weather data.


Rotational Invariant Human Faces Detection in Color Images

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.


A Design of Emotional Classification System and Emotional Space Map

In this paper, we propose a new emotional speech classi cation method and new space map generation method by analyzing feature parameters obtained from the emotional speech and using neural networks. In many cases, emotions are found by the change of pitches. Input values of neural networks (NNs) are then emotional pitch patterns, which are time-varying. It is shown that NNs can achieve emotional speech classi cation and generation of space map by learning each emotional pitch pattern by means of computer simulations. Finally, many tests for emotional speech classi cation are done by using the obtained emotional space map.


Searching of Liver Ranges in CT Images by Using Genetic Algorithms and Neural Networks

Recently, internal human organ disorders that medical image analysis can be used to detect is being actively re searched. The research, however, has concentrated on the extraction of pulmonary tumors. There is, therefore, little research being done on the extraction of liver tumors. This is because there is no difference between concentrated values of a healthy part and one with a tumor in liver CT images. In this paper, the extraction method of such liver tumors is proposed. Furthermore, in order to demonstrate the effectiveness of the proposed scheme, we show simulation examples, using real CT image data.


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.


Fast Face Detection System Using the GA-based Threshold Method

Threshold selection in multi-value images is performed based on their color information. When the threshold in an image is fixed it lacks versatility for the others. Because the color information varies under the influence of light conditions. In this paper, a genetic algorithm (GA) is used to select the most likely values of lips and skin colors in a light condition. It is possible to extract objects from the multi-value image only with the color information. In this paper, the objects of extraction are chosen to be the human lips and skin colors.


Emotional Speech Classification with Prosodic Prameters by Using Neural Networks

Interestingly, in order to achieve a new Human Interface such that digital computers can deal with the KASEI information, the study of the KANSEI information processing recently has been approached. In this paper, we propose a new classification method of emotional speech by analyzing feature parameters obtained from the emotional speech and by learning them using neural networks, which is regarded as a KANSEI information processing. In the present research, KANSEI information is usually human emotion. The emotion is classified broadly into four patterns such as neutral, anger, sad and joy. The pitch as one of feature parameters governs voice modulation, and can be sensitive to change of emotion. The pitch is extracted from each emotional speech by the cepstrum method. Input values of neural networks (NNs) are then emotional pitch patterns, which are time-varying. It is shown that NNs can achieve classification of emotion by learning each emotional pitch pattern by means of computer simulations.


An Extraction Method of Liver Tumors by Using Genetic Algorithms and Neural Networks

Recently, internal human organ disorders that medical image analysis can be used to detect is being actively researched. The research have however, concentrated on the extraction of pulmonary tumors. There is therefore, little research being done on the extraction of liver tumors. This is because there is no difference between concentrated values of a healthy part and one with a tumor in liver CT images. In this paper, the extraction method of such liver tumors is proposed. Furthermore, in order to demonstrate the effectiveness of the proposed scheme, we show a simulation example, using real CT image data.


A Knowledge Processing Method in Neural Networks

Proc. of Knowledge- Based Intelligent Information Engineering Systems & Allied Technologies

本国際会議論文は，ニューラルネットワークで知識処理を行うための一方法を提案している．まず，ニューラルネットワークに人間が有する知識を埋め込み，知識洗練化のための学習を行い，学習後にルールを抽出する方法である．本手法の有効性を検証するために，パターン分類に対する計算機シミュレーションを行い，人間が理解できるルールを検出できることを検証している．


Classification of Emotional Speech by Using Neural Networks

本国際会議論文は，ニューラルネットワークにより人間の音声が有する感情を抽出するための方法を提案している．まず，発生音声から音声の基本情報であるピッチを抽出し，その時間的変化をニューラルネットワークにより識別し感情判定を行う．感情音声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，様々な条件での性能比較を行っている．


A Design of the Fast Face Detection System by the GA-Based Threshold

本国際会議論文は，遺伝的アルゴリズムによりカラー画像中の人間の顔を検出するための方法を提案している．まず，カラー顔画像の肌色範囲を画像ごとに決定し，その情報に基づいて肌色閾値を表す方程式を決定する．この方程式により様々な輝度画像での肌色範囲を高速に精度よく決定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ている．


Development of Speedy and High Sensitive Pen System for Writing Pressure and Writer Identification

高速に本人を識別するバイオメトリックな手法である筆者認識のためのハードウエアとソフトウエアを構築した．ハードウエアとして，電磁的に位置を検出するペンを開発した．ソフトウエアとして，個人を識別するためにニューラル·ネッオワークによる手法とルールに基づく手法を開発した．筆者識別能力の高い本システムは実用性があると言える．


Knowledge Incorporation and Rule Extraction in Neural Networks

Proc. of International Conference on Artificial Neural Networks

In this paper a new knowledge incorporation and rule extraction method in neural networks is presented. The rule form of an if-then type can be inserted into a neural network (NN) as knowledge of a problem. NN is then trained by using a set of training samples. In this case the structure learning algorithm with forgetting is used to generate a small-sized NN system. After the NN training, rules are extracted from it. The results of computer simulations show that this approach can generate obvious network architectures and as a result simple rules compared with conventional rule extraction methods.


A Detection Method of Face Regions in Color Images by Using Evolutionally Computation

Proc. of International Joint Conference on Neural Networks

This paper presents a new scheme to design a face detection system by using a genetic algorithm (GA). In particular, the object of this paper is to design a fast face detection system. 5 human emotions are considered; neutrality, happiness, sadness, anger and surprise. This paper pays attention to lip forms in human characteristics. Furthermore, in order to decrease the detection time, GA is used for the purpose of focusing on the lips in images. Finally, in order to demonstrate the effectiveness of the proposed scheme, we show simulation examples.


Automatic Extraction of Tumors by Using Neural Computation

Automatic extraction of tumors is needed in a medical field. A 3D image is composed of the sequence of 2D CT images. The extraction of a liver field is difficult, because the CT images include various internal organs. In this paper, an improved active net is proposed to analyze the information of CT images. It is shown this method has the ability as a pre-processing system for extraction of liver tumors.


Rainfall Forecast Using a Neural Network with a Genetic Preprocessing

Proc. of Knowledge- Based Intelligent Information Engineering Systems & Allied Technologies

本国際会議論文は，遺伝的手法により地域的な降雨の予測を高精度に行う方法を提案している．気象データには多数の属性が含まれるが，その中で真に必要な属性の選択を遺伝的手法により行い，ニューラルネットワークにより予測するシステムである．大阪の気象データを用いる計算機シミュレーションにより，その特徴選択特性と予測精度の点での有効性を定量的に検証している．


Rainfall Forecast Using a Neural Network and a Genetic Algorithm

Utilization of Soft Computing Techniques for Intelligent Prediction


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．


A Design of Face Detection System in Color Images by Using Evolutionary Computation

We propose a new method to examine whether or not human faces are included in color images by using a lip detection neural network (LDNN) and a skin distinction neural network (SDNN). In conventional methods, if there are the same color as the skin color in scenes, the domain which is accepted as not only the skin color but any other color can be searched. However, first, the lip are detected by LDNN in the proposed method. Next, SDNN is utilized to distinguish the skin color from the others. The proposed method can obtain relatively high recognition accuracy, since it have the double recognition structure of LDNN and SDNN. Furthermore, in order to reduce the amount of calculation and to discriminate a skin color and the other colors, GA can search to minimize the number of necessary data. Then a storage capacity and the amount of operations can be cut down by using GA and LDNN and SDNN are trained by using the reduced data. Finally, in order to demonstrate the effectiveness of the proposed scheme, computer simulations were performed. First, 100 lip color, 100 skin color and 100 background pictures, which are changed into 10×10 pixels, are prepared for training. The validity was verified by testing images containing several faces.


Neural Net based Image Retrieval by Using Color and Location Information

Proc. of the 2000 IEEE International Conference on Systems, Man and Cybernetics

A neural net-based image retrieval method is presented, in which color and location features are extracted from images. This method can retrieve similar images to a selected one from a large data set of color images. In particular, the location features in the color distribution of an image are important in the image retrieval. This image retrieval method extracts color features and their location information included in an image. A neural network tries to find images with similar features from a data set. First, images are translated into gray-scale ones and then are divided into eight regions based on gray-scale values. The color and location features are extracted from these regions after integration of regions. The RGB and HSV color values in each region, area, and the X- and Y-values in the orthogonal coordinates are learned by a multi-layered neural network. After learning, the neural network evaluates the similarity between a selected image and the other ones in the data set. The similar images found by the neural network are the retrieval results.