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检索条件"任意字段=IEE Colloquium on Neural Networks for Image Processing Applications"

共 208 条记录，以下是21-30 订阅

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Model-based gait analysis for gender recognition

Model-based gait analysis for gender recognition

引用

2012 ieeE 8th International colloquium on Signal processing and Its applications, CSPA 2012

作者： Ismail, Ahmad Puad Tahir, Nooritawati Md Hussain, Aini Faculty of Electrical Engineering Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia 43600 Bangi Selangor Malaysia

ISBN: (纸本)9781467309615

Gender classification via model-based human gait data is still immature. Hence in this research, the possibility of side view human gait silhouette to be used as gender recognition is evaluated using model-based approach. Firstly, six attributes located at lower part of human gait specifically from below waist onwards have been identified as the significant points are skeletonized based on the human gait silhouette attained. Next the vertical angles of both hip and knee with respect to thigh for 32 image sequences are extracted as feature vectors followed by feature selection via statistical analysis specifically analysis of variance along with multiple comparison procedure. Further, the resultant of feature selection acted as inputs to the artificial neural network classifier. Initial findings with accuracy of 90% and above confirmed that the proposed method suited to be utilized as gender recognition based on human gait. © 2012 ieeE.

关键词： neural networks

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Intelligent brain hemorrhage diagnosis using artificial neural networks

Intelligent brain hemorrhage diagnosis using artificial neur...

引用

2012 ieeE Business, Engineering and Industrialapplications colloquium, BEIAC 2012

作者： Balasooriya, Ushani Perera, M.U.S. Colombo Sri Lanka

ISBN: (纸本)9781467304269

Brain hemorrhage is a type of stroke which is caused by an artery in the brain bursting and causing bleeding in the surrounded tissues. Diagnosing brain hemorrhage, which is mainly through the examination of a CT scan enables the accurate prediction of disease and the extraction of reliable and robust measurement for patients in order to describe the morphological changes in the brain as the recovery progresses. Though a lot of research on medical image processing has been done, still there is opportunity for further research in the area of brain hemorrhage diagnosis due to the low accuracy level in the current methods and algorithms, coding complexity of the developed approaches, impracticability in the real environment, and lack of other enhancements which may make the system more interactive and useful. Additionally many of the existing approaches address the diagnosis of a limited no of brain hemorrhage types. This project investigates the possibility of diagnosing brain hemorrhage using an image segmentation of CT scan images using watershed method and feeding of the appropriate inputs extracted from the brain CT image to an artificial neural network for classification. The output generated as the type of brain hemorrhages, can be used to verify expert diagnosis and also as a learning tool for trainee radiologists to minimize errors in current methods. The prototype developed using Matlab can help medical students to practice the related concepts they learn using an image guide with examples for surgeries and surgical simulation. System was evaluated by the domain experts, like radiologists, intended users such as medical students as well as by technical experts. The prototype developed was successful since it was being evaluated as credible, innovative and useful software for the students in the field of radiology while 100% of the evaluators mentioned the diagnosis accuracy is acceptable. © 2012 ieeE.

关键词： Diagnosis

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Intelligent brain hemorrhage diagnosis using artificial neural networks

Intelligent brain hemorrhage diagnosis using artificial neur...

引用

ieeE Business Engineering and Industrial applications colloquium (BEIAC)

作者： Ushani Balasooriya M. U. S Perera Department of Computing Informatics Institute of Technology Colombo Sri Lanka

关键词： Hemorrhaging Computed tomography image segmentation Feature extraction Training Biomedical imaging Magnetic resonance imaging

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iPepper: Intelligent pepper grading and quality assurance system

iPepper: Intelligent pepper grading and quality assurance sy...

引用

International colloquium on Signal processing & Its applications, CSPA

作者： D.N.F Awang Iskandar Rubiyah Baini Alvin Yeo Wee Shapiee Abdul Rahman Ahmad Hadinata Fauzi Faculty of Computer Science and Information Technology Universiti Malaysia Sarawak Kota Samarahan Sarawak Malaysia Faculty of Engineering Universiti Malaysia Sarawak Kota Samarahan Sarawak Malaysia

Pepper is a key export of the state of Sarawak (Malaysian Borneo); it produces 98% of Malaysia's pepper. At present, processed pepper berries are graded manually. This process is time consuming and error prone as it is very much dependent on the experience of the pepper grader. To overcome these weaknesses, we propose a Pepper Grading System which employs image processing and machine learning approaches using image features and moisture content data of the pepper berries. For instance, from initial tests, a high correlation between the grade of pepper berries to the colour features has been detected. Using existing machine learning algorithms in WEKA, we have obtained a 100% accuracy in categorising the pepper berries into the correct grades. In addition, moisture content and colourometer readings provide another 2 other parameters which may complement the image features in accurately classifying the berries into the right grades.

关键词： image color analysis Moisture Machine learning algorithms Feature extraction Artificial neural networks Testing

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Recognizing motor imagery using dynamic cascade feed-forward neural networks

Recognizing motor imagery using dynamic cascade feed-forward...

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International colloquium on Signal processing & Its applications, CSPA

作者： C. R. Hema School of Mechatronic Engineering Universiti Malaysia Pedis Pauh Perlis Malaysia

Recognition of Motor imagery (MI) using a dynamic cascade feed-forward neural network (CFNN) is presented. MI is the mental simulation of a motor act that includes preparation for movement and mental operations of motor representations implicitly or explicitly. The ability of an individual to control his EEG through imaginary motor tasks enables him to control devices through a Brain Machine Interface (BMI). A BMI design using the CFNN is used to discriminate EEG signals acquired during MI for four states namely, relax, forward, left and right. EEG is recorded at the C3 and C4 locations using noninvasive scalp electrodes placed over the motor cortex. The proposed CFNN is tested with data collected from 10 subjects. Average recognition accuracy of 93.3% validates the proposed four-state BMI design using MI and CFNN.

关键词： image recognition Feedforward systems neural networks Feedforward neural networks Electroencephalography Biological neural networks Brain modeling Signal design Scalp Electrodes

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Comparison of performance using Daubechies Wavelet family for facial expression recognition

Comparison of performance using Daubechies Wavelet family fo...

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International colloquium on Signal processing & Its applications, CSPA

作者： M. Satiyan M. Hariharan R. Nagarajan School of Mechatronic Engineering Universiti Malaysia Pedis Perlis Malaysia

ISBN: (纸本)9781424471218

This paper investigates the performance of a Daubechies Wavelet family in recognizing facial expressions. A set of luminance stickers were fixed on subject's face and the subject is instructed to perform required facial expressions. At the same time, subject's expressions are recorded in video. A set of 2D coordinate values are obtained by tracking the movements of the stickers in video using tracking software. Daubechies wavelet transform with different orders (db1 to db20) performed on obtained data. Standard deviation is derived from wavelet approximation coefficients for each daubechies wavelet orders. This standard deviation is used as an input to the neural network for classifying 8 facial expressions.

关键词： Face recognition Gold Testing Artificial neural networks Spatial databases image databases image recognition image sequences Eyebrows Infrared detectors

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Detection of facial changes for hospital ICU patients using neural network

Detection of facial changes for hospital ICU patients using ...

引用

International colloquium on Signal processing & Its applications, CSPA

作者： Muhammad Naufal Bin Mansor Sazali Yaacob R. Nagarajan M. Hariharan School of Mechatronic Engineering Universiti Malaysia Perlis Jejawi Perlis Malaysia

ISBN: (纸本)9781424471218;9781424471225

This paper presents an integrated system for detecting facial changes of patient in a hospital in Intensive Care Unit (ICU). The facial changes are most widely represented by eyes movements. The proposed system uses color images and it consists of three modules. The first module implements skin detection to detect the face. The second module constructs eye maps that are responsible for changes in eye regions. The third module extracts the features of eyes by processing the image and measuring certain demensions of eyes regions. Finally a neural network classifier used to classify the motion of eyes either it open, half open or close. From 300 samples of face images, it is found that the maximum classification accuracy of 93.33% was obtained for the proposed features and classification technique.

关键词： Face detection Hospitals neural networks

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Estimation of Mobile Robot Orientation Using neural networks

Estimation of Mobile Robot Orientation Using Neural Networks

引用

5th International colloquium on Signal processing and Its applications (CSPA)

作者： Paulraj, M. P. Ahmad, R. Badlishah Hema, C. R. Hashim, Fadzilah Univ Malaysia Perlis Sch Mechatron Engn Perlis Malaysia Univ Malaysia Perlis Sch Comp & Commun Engn Perlis Malaysia

ISBN: (纸本)9781424441518

The computation of a mobile robot position and orientation is a common task in the area of computer vision and image processing. For a successful application, it is important that the position and orientation of a mobile robot must be determined properly. In this paper, a simple procedure for determining the orientation of the mobile robot using two cameras is presented. The two cameras are used to capture the images of a mobile robot at various orientations. Four simple neural network models are developed to associate the inputs and output (orientation). First and second neural network models are used to estimate the orientation of a mobile robot using only the features derived from the first and second camera. The third neural network model is used for estimating the orientation of a mobile robot using features derived from both the first and second camera. The fourth neural network model is used to estimate the orientation using features derived from the combined images. Simulation results show that the proposed algorithm can be used to estimate the orientation of the mobile robot accurately.

关键词： neural network mobile robot orientation stereo imaging

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Estimation of mobile robot orientation using neural networks

Estimation of mobile robot orientation using neural networks

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International colloquium on Signal processing & Its applications, CSPA

作者： M.P. Paulraj R. Badlishah Ahmad C.R. Hema Fadzilah Hashim School of Mechatronic Engineering Universiti Malaysia Perlis Perlis Malaysia School of Computer and Communication Engineering Universiti Malaysia Perlis Perlis Malaysia

关键词： Mobile robots neural networks Robot vision systems Cameras Sonar navigation Robot sensing systems Floors Application software Computer vision Signal processing

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Gesture recognition system for Kod Tangan Bahasa Melayu (KTBM) using neural network

Gesture recognition system for Kod Tangan Bahasa Melayu (KTB...

引用

International colloquium on Signal processing & Its applications, CSPA

作者： M. P. Paulraj Sazali Yaacob Hazry Desa Wan Mohd Ridzuan Wan Ab Majid School of Mechatronic Engineering Universiti Malaysia Perlis (UniMAP) Perlis Malaysia

This paper presents simple methods for translating Kod Tangan Bahasa Melayu (KTBM) into voice signal based on subject head and two hand gestures. Different gesture signs made by different subjects are captured using a USB Web camera in RGB video stream format with a screen bit depth of 24 bits and a resolution of 320 times 240 pixels. The recorded video of the sign language is divided into number of image frames. Using a simple segmentation technique, the frame image is segmented into three region namely, head region, left hand region and right hand region. After performing the image segmentation, the image frames are converted into binary image format. A simple feature extraction method is then applied and the variations of the features in the subsequent frame are modeled using discrete cosine transform (DCT). The features extracted are associated to the equivalent voice sound and a simple neural network model trained by error prob method is developed. An audio system is used to play the equivalent voice signal from the recognized sign language. Experimental results demonstrate that the recognition rate of the proposed neural network models is about 81.07%.

关键词： neural networks image segmentation Magnetic heads Handicapped aids Feature extraction Discrete cosine transforms Universal Serial Bus Cameras Streaming media Signal resolution

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