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Journal of Information Science and engineering: Editorial notice

Journal of Information Science and engineering 2005年第5期21卷 i-ii页

作者： Lee, Der-Tsai Amato, Nancy M. Chang, Shih-Fu Chen, Homer H. Chen, Tsuhan Hsu, Tsan-Sheng Hwang, Jenq-Neng Kuo, Sy-Yen Kuo, Tei-Wei Li, Chung-Sheng Tokuyama, Takeshi Wing, Jeannette Wu, Tzong-Chen Yen, John Computer Science at Texas A and M University United States Parasol Laboratory United States IEEE Transactions on Parallel and Distributed Systems Computing Research Association's Committee on the Status of Women in Computing Research CRA-W's Distributed Mentor Program United States Department of Electrical Engineering Columbia University United States Lab. United States College of Electrical Engineering and Computer Science National Taiwan University Taiwan IEEE Transactions on Circuits and Systems for Video Technology IEEE Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA United States ACM ACM SIGACT IEEE Computer Society IICM Austria Research and Development of the Department Multimedia Signal Processing Technical Committee IEEE Signal Processing Society United States IEEE Transactions on Circuits and systems for Video Technology College of Electrical Engineering and Computer Science National Taiwan Ocean University Keelung Taiwan Department of Electrical Engineering National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan IEEE Technical Committee on Real-Time Systems Computer Science Division IBM T.J. Watson Research Center United States IBM Research Division Graduate School of Information Sciences Tohoku University Japan ACM IPSJ Mathematical Society of Japan Japan Department of Computer Science Computer Science Department Carnegie Mellon University United States National Academies of Science's Computer Science and Telecommunications Board United States Microsoft's Trustworthy Academic Advisory Board Intel Research Pittsburgh's Advisory Board United States Dartmouth's Institute for Security Technology Studies Advisory Committee Canada Sloan Research Fellowships Program Committee United States ACM Taiwan China Information Sciences and Technology Pennsylvania State University United States Laboratory for Intelligent Agents Penn State's School of Info

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Associative memory based on ratio learning for real time skin color detection 32

Associative memory based on ratio learning for real time ski...

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32nd Applied Imagery Pattern Recognition Workshop, AIPR 2003

作者： Seow, Ming-Jung Asari, V.K. VLSI Systems Laboratory Department of Electrical and Computer Engineering Old Dominion University NorfolkVA23529 United States

ISBN: (纸本)0769520294

A novel approach for skin color modeling using ratio rule learning algorithm is proposed in this paper. The learning algorithm is applied to a real time skin color detection application. The neural network learn, based on the degree of similarity between the relative magnitudes of the output of each neuron with respect to that of all other neurons. The activation/threshold function of the network is determined by the statistical characteristic of the input patterns. Theoretical analysis has shown that the network is able to learn and recall the trained patterns without much problem. It is shown mathematically that the network system is stable and converges in all circumstances for the trained patterns. The network utilizes the ratio-learning algorithm for modeling the characteristic of skin color in the RGB space as a linear attractor. The skin color will converge to a line of attraction. The new technique is applied to images captured by a surveillance camera and it is observed that the skin color model is capable of processing 420x315 resolution images of 24-bit color at 30 frames per second in a dual Xeon 2.2 GHz CPU workstation running Windows 2000. © 2004 IEEE.

关键词： Learning algorithms

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Neural network based skin color model for face detection 32

Neural network based skin color model for face detection

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32nd Applied Imagery Pattern Recognition Workshop, AIPR 2003

作者： Seow, Ming-Jung Valaparla, D. Asari, V.K. VLSI Systems Laboratory Department of Electrical and Computer Engineering Old Dominion University NorfolkVA23529 United States

ISBN: (纸本)0769520294

This paper presents a novel neural network based technique for face detection that eliminates limitations pertaining to the skin color variations among people. We propose to model the skin color in the three dimensional RGB space which is a color cube consisting of all the possible color combinations. Skin samples in images with varying lighting conditions, from the Old Dominion University skin database, are used for obtaining a skin color distribution. The primary color components of each plane of the color cube are fed to a three-layered network, trained using the backpropagation algorithm with the skin samples, to extract the skin regions from the planes and interpolate them so as to provide an optimum decision boundary and hence the positive skin samples for the skin classifier. The use of the color cube eliminates the difficulties of finding the non-skin part of training samples since the interpolated data is consider skin and rest of the color cube is consider non-skin. Subsequent face detection is aided by the color, geometry and motion information analyses of each frame in a video sequence. The performance of the new face detection technique has been tested with real-time data of size 320x240 frames from video sequences captured by a surveillance camera. It is observed that the network can differentiate skin and non-skin effectively while minimizing false detections to a large extent when compared with the existing techniques. In addition, it is seen that the network is capable of performing face detection in complex lighting and background environments. © 2004 IEEE.

关键词： Color

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Face detection technique based on rotation invariant wavelet features

Face detection technique based on rotation invariant wavelet...

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International Conference on Information Technology: Coding and Computing (ITCC)

作者： Satyanadh Gundimada Vijayan Asari VLSI Systems Laboratory Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

ISBN: (纸本)0769521088

A rotation and scale invariant face detection algorithm based on the texture of a human face is proposed. Wavelet packet analysis is performed on the test image to get the coefficients. It is observed that wavelet packet decomposition until third level is sufficient enough to get the necessary frequencial components essential for classifying faces and nonfaces. Rotation invariant textural features are extracted from the wavelet coefficients. A scale invariant distance measure between the feature vectors of each of the candidate faces and the prototype face image is proposed to classify the candidate faces into faces and nonfaces. The detection process implements a nonlinear luminance based lighting compensation method, which is very efficient in enhancing and restoring the natural colors into the images taken in darker and varying lighting environments. The novel detection process proposed is highly efficient in terms of speed and accuracy in detecting frontal view faces in a complex background. The face detection performance of the proposed system is comparable to other reported leading systems.

关键词： Face detection Wavelet packets Humans Wavelet analysis Image analysis Performance analysis Performance evaluation Testing Frequency Feature extraction

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Design of an efficient architecture for real-time image enhancement based on a luma-dependent nonlinear approach

Design of an efficient architecture for real-time image enha...

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International Conference on Information Technology: Coding and Computing (ITCC)

作者： Hau Ngo Li Tao V. Asari VLSI Systems Laboratory Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

A luminance dependent nonlinear image enhancement algorithm is proposed in this paper. Luma-dependent nonlinear enhancement (LDNE) is a luminance based multi-scale center/surround retinex algorithm, which achieves simultaneous dynamic range compression, color consistency and lightness rendition. Compared to other image enhancement algorithms, LDNE provides better color fidelity, specifies fewer parameters, enhances less noise, prevents the unwanted luminance drop at the uniform luminance areas, keeps the 'bright' background unaffected, and enhances well the 'dark' objects in 'bright' background. To support real time applications, an FPGA based co-processor is designed to perform some of the most computationally intensive operation in LDNE algorithm. Estimation techniques are introduced in the hardware algorithmic designed to achieve faster, simpler and more efficient architecture which is capable of processing more than 21 frames per second.

关键词： Image enhancement Colored noise Algorithm design and analysis Dynamic range Image coding Background noise Field programmable gate arrays Coprocessors Hardware computer architecture

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A robust face recognition system for real time surveillance

A robust face recognition system for real time surveillance

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International Conference on Information Technology: Coding and Computing (ITCC)

作者： M.J. Seow R. Gottumukkal D. Valaparla K.V. Asari VLSI Systems Laboratory Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

This paper describes a face detection and recognition system in color image sequences with a novel scheme to model skin color in the RGB color-space using neural networks. In our approach, there are no limitations regarding human skin color. This method eliminates the difficulty of describing non-skin samples by approximating non-skin color from skin samples in the vlsi systems laboratory skin database. The neural network algorithm based face detection is performed by using a multilayer feed-forward neural network trained with back-propagation learning algorithm in conjunction with a modular approach utilizing the distance based learning for reducing the structural complexity of the network by analyzing each frame in the video sequence. The recognition is performed based on composite principal component analysis (CPCA) algorithm. This algorithm is better equipped to recognize faces under the conditions of varying illumination and pose compared to the conventional PCA. The system is capable of detecting and recognizing faces at the rate of 10 frames per second when the frame resolution is 320 /spl times/ 240 and the color depth is 24-bit.

关键词： Robustness Face recognition Real time systems Surveillance Face detection Skin Neural networks Multi-layer neural network Feedforward neural networks Principal component analysis

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An integrated neighborhood dependent approach for nonlinear enhancement of color images

An integrated neighborhood dependent approach for nonlinear ...

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International Conference on Information Technology: Coding and Computing (ITCC)

作者： L. Tao Vijayan Asari VLSI Systems Laboratory Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

We propose a new image enhancement algorithm INDANE (integrated neighborhood dependent approach for nonlinear enhancement of color images), which is applied to improve the visibility of the dark regions in digital images. INDANE is a combination of two independent processes: luminance enhancement and contrast enhancement. The luminance enhancement, also regarded as a process of dynamic range compression, is essentially an intensity transformation based on a specifically designed nonlinear transfer function. The contrast enhancement transforms the pixel's intensity based on the relationship between the center pixel and its surrounding pixels. For color images, they are firstly converted to intensity (grayscale) images, and then are treated by the proposed image enhancement process.

关键词： Color Dynamic range Convolution Image enhancement Image coding Transfer functions Pixel Digital images Image converters Gray-scale

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L2-norm approximation based learning in recurrent neural networks for expression invariant face recognition

L2-norm approximation based learning in recurrent neural net...

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IEEE International Conference on systems, Man and Cybernetics

作者： Ming-Jung Seow D. Valaparla V.K. Asari VLSI Systems Laboratory Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

A new learning algorithm based on L2-norm approximation to define the relationship between two neurons in a recurrent neural network is proposed in this paper. The learning process utilizes the statistical relationship between each component of the input pattern with respect to every other component. The activation function of a neuron is a rectangular function whose position changes adaptively with respect to the input pattern and its left and right wings are decided by the mean of maximum variations of the training signals to that neuron. The new training algorithm is applied for recognition of faces images with varying expressions. 975 face images of 13 persons from the Carnegie Mellon University (CMU) face expression variant database are used for evaluating the performance of the network. The network has been trained with 5 images and tested with the remaining 70 images of each person. The recurrent neural network with the new learning algorithm recognized all the 13 persons in this database without error.

关键词： Intelligent networks Recurrent neural networks Face recognition Pattern recognition Neurons Biological neural networks Biology computing Artificial neural networks Biological system modeling Approximation algorithms

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Associative memory based on ratio learning for real time skin color detection

Associative memory based on ratio learning for real time ski...

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Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Ming-Jung Seow V.K. Asari VLSI Systems Laboratory Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

A novel approach for skin color modeling using ratio rule learning algorithm is proposed in this paper. The learning algorithm is applied to a real time skin color detection application. The neural network learn, based on the degree of similarity between the relative magnitudes of the output of each neuron with respect to that of all other neurons. The activation/threshold function of the network is determined by the statistical characteristic of the input patterns. Theoretical analysis has shown that the network is able to learn and recall the trained patterns without much problem. It is shown mathematically that the network system is stable and converges in all circumstances for the trained patterns. The network utilizes the ratio-learning algorithm for modeling the characteristic of skin color in the RGB space as a linear attractor. The skin color will converge to a line of attraction. The new technique is applied to images captured by a surveillance camera and it is observed that the skin color model is capable of processing 420/spl times/315 resolution images of 24-bit color at 30 frames per second in a dual Xeon 2.2 GHz CPU workstation running Windows 2000.

关键词： Associative memory Skin Neurons Image converters Color Neural networks Pattern analysis Surveillance Cameras Image resolution

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Neural network based skin color model for face detection

Neural network based skin color model for face detection

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Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Ming-Jung Seow D. Valaparla V.K. Asari VLSI Systems Laboratory Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

This paper presents a novel neural network based technique for face detection that eliminates limitations pertaining to the skin color variations among people. We propose to model the skin color in the three dimensional RGB space which is a color cube consisting of all the possible color combinations. Skin samples in images with varying lighting conditions, from the Old Dominion University skin database, are used for obtaining a skin color distribution. The primary color components of each plane of the color cube are fed to a three-layered network, trained using the backpropagation algorithm with the skin samples, to extract the skin regions from the planes and interpolate them so as to provide an optimum decision boundary and hence the positive skin samples for the skin classifier. The use of the color cube eliminates the difficulties of finding the non-skin part of training samples since the interpolated data is consider skin and rest of the color cube is consider non-skin. Subsequent face detection is aided by the color, geometry and motion information analyses of each frame in a video sequence. The performance of the new face detection technique has been tested with real-time data of size 320/spl times/240 frames from video sequences captured by a surveillance camera. It is observed that the network can differentiate skin and non-skin effectively while minimizing false detections to a large extent when compared with the existing techniques. In addition, it is seen that the network is capable of performing face detection in complex lighting and background environments.

关键词： Neural networks Skin Face detection Video sequences Image databases Backpropagation algorithms Data mining Image color analysis Information geometry Motion analysis

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