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2002 Congress on Evolutionary Computation, CEC 2002

作者： Wai, Yin Yee Chow, Chi Kin Lee, Tong Computer Vision and Image Processing Laboratory Dept. of Electronic Engineering Chinese University of Hong Kong Shatin Hong Kong

ISBN: (纸本)0780372824

There is considerable interest in motion capture from an image sequence taken from a video camera. However, since the images only consist of 2D information, the distance of an object from the image plane cannot be determined uniquely unless some constraints are imposed. Coplanar motion is a popular constraint adopted for this purpose. If the captured motion is not coplanar, only two possible solutions can be obtained at the best even the actual size of the object is given. Therefore, the motion cannot be captured without resolving the distances of each control point from the image plane. By assuming smooth transition, the problem is formulated as one minimizing the transition between image frames and the application of GA to solve this optimization problem is proposed. © 2002 IEEE.

关键词： Video cameras

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Multiple objects segmentation based on maximum-likelihood estimation and optimum entropy-distribution(MLE-OED)

Multiple objects segmentation based on maximum-likelihood es...

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16th International Conference on Pattern Recognition, ICPR 2002

作者： Jun, Xie Tsui, H.T. Deshen, Xia Image Processing and Computer Vision Lab Department of Electronic Engineering Chinese University of Hong Kong Hong Kong Pattern Recognition Lab Department of Computer Science Nanjing University of Sci.andTech. China

ISBN: (纸本)0769516963

A new method based on MLE-OED is proposed for unsupervised image segmentation of multiple objects which have fuzzy edges. It adjusts the parameters of a mixture of Gaussian distributions via minimizing a new loss function proposed to implement image segmentation based on the image's local spatial information and global intensity distribution properties . The loss function consists of two terms: a local content fitting term, which optimizes the entropy distribution, and a global statistical fitting term, which maximizes the likelihood of the parameters for the given data. The proposed segmentation method was validated by simulated and real examples Its performance in the experiments is better than those of two popular methods. © 2002 IEEE.

关键词： Maximum likelihood estimation

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Construction of multi-layer feedforward binary neural network by a genetic algorithm

Construction of multi-layer feedforward binary neural networ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Chi Kin Chow Tong Lee Computer Vision and Image Processing Laboratory Department of Electronic Engineering Chinese University of Hong Kong Hong Kong China

An approach is introduced to determine the topology of a feedforward binary neural network automatically. The approach is based on a construction algorithm that constructs one layer of hidden nodes at a time until the problem is solved. In each layer, the algorithm determines the necessary number of nodes through a growth process by finding the best hidden node that would help to partition the input training data set. This is done using a genetic algorithm. The proposed algorithm can determine the necessary number of hidden layers and number of hidden nodes at each layer automatically. Tests on a number of benchmark problems illustrated the effectiveness of the proposed technique, both in terms of network complexity and recognition accuracy, compared with a geometrical learning approach.

关键词： Neural networks Multi-layer neural network Feedforward neural networks Genetic algorithms Network topology Partitioning algorithms Training data Error correction computer vision image processing

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Invariant feature matching by Hopfield-type neural network

Invariant feature matching by Hopfield-type neural network

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International Joint Conference on Neural Networks (IJCNN)

作者： Wen-Jing Li Tong Lee Computer Vision and Image Processing Laboratory Department of Electronic Engineering Chinese University of Hong Kong Hong Kong China

In this paper, a novel Hopfield model for silhouette matching invariant to projective transformations is proposed. Although the new network has higher-order energy function, we show that it can be solved using a standard second-order Hopfield network, by taking advantage of the neighborhood information in the data. The experimental results with real data show that the proposed method can provide accurate matching results in image registration and object recognition.

关键词： Neural networks Hopfield neural networks Object recognition Power engineering and energy Stereo vision Motion estimation Feature extraction Cost function Layout computer vision

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Resolving ambiguity in depth extraction for motion capture using genetic algorithm

Resolving ambiguity in depth extraction for motion capture u...

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Congress on Evolutionary Computation

作者： Yin Yee Wai Chi Kin Chow Tong Lee Computer Vision and Image Processing Laboratory Department of Electronic Engineering Chinese University of Hong Kong Sha Tin Hong Kong China

关键词： Genetic algorithms Humans Cameras image resolution image sequences Animation Motion analysis Optical filters Optical recording Optical sensors

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Multiple objects segmentation based on maximum-likelihood estimation and optimum entropy-distribution (MLE-OED)

Multiple objects segmentation based on maximum-likelihood es...

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International Conference on Pattern Recognition

作者： Xie Jun H.T. Tsui Xia Deshen Image Processing and Computer Vision Laboratory Department of Electronic Engineering Chinese University of Hong Kong Hong Kong China Pattern Recognition Laboratory Department of Computer Science Nanjing University of Science and Technology China

ISBN: (纸本)076951695X

A new method based on MLE-OED is proposed for unsupervised image segmentation of multiple objects which have fuzzy edges. It adjusts the parameters of a mixture of Gaussian distributions via minimizing a new loss function proposed to implement image segmentation based on the image's local spatial information and global intensity distribution properties. The loss function consists of two terms: a local content fitting term, which optimizes the entropy distribution, and a global statistical fitting term, which maximizes the likelihood of the parameters for the given data. The proposed segmentation method was validated by simulated and real examples. The performance in the experiments is better than those of two popular methods.

关键词： Object segmentation Maximum likelihood estimation image segmentation Layout Uncertainty image edge detection Shape measurement image processing Pattern recognition Gaussian distribution

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Multiple objects segmentation based on Maximum-likelihood Estimation and Optimum Entropy-distribution(MLE-OED)

Proceedings - International Conference on Pattern Recognitio...

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Proceedings - International Conference on Pattern Recognition 2002年第1期16卷 707-710页

A new method based on MLE-OED is proposed for unsupervised image segmentation of multiple objects which have fuzzy edges. It adjusts the parameters of a mixture of Gaussian distributions via minimizing a new loss function proposed to implement image segmentation based on the image's local spatial information and global intensity distribution properties . The loss function consists of two terms: a local content fitting term, which optimizes the entropy distribution, and a global statistical fitting term, which maximizes the likelihood of the parameters for the given data. The proposed segmentation method was validated by simulated and real examples. Its performance in the experiments is better than those of two popular methods. © 2002 IEEE.

关键词： Object recognition

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image approximation and smoothing by support vector regression

Image approximation and smoothing by support vector regressi...

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International Joint Conference on Neural Networks (IJCNN)

作者： D. Kai Tik Chow Tong Lee Computer Vision and Image Processing Laboratory Department of Electronic Engineering Chinese University of Hong Kong Hong Kong China

ISBN: (纸本)0780370449

A new image representation by support vector regression (SVR) is introduced. After a grey level image is approximated as a continuous function using SVR, which maps a 2D pixel coordinate input into a 1D pixel grey level output, the image can then be expressed in terms of the extracted support vectors and their corresponding Lagrange multipliers. The image is reconstructed by a linear combination of kernels with weights equal to the values of Lagrange multipliers. With support vector representation, we can observed that: 1) it is able to remove noise from image, the denoising effect of SVR representation is implicit during image encoding, and it can be controlled by the SVR training parameters; 2) if a Gaussian RBF kernel is used in SVR representation, Gaussian smoothing can be easily implemented by increasing the variance of kernel during image reconstruction and sharpening can be done by reducing the variance.

关键词： Smoothing methods Kernel Pixel Vectors Lagrangian functions image reconstruction Noise reduction image representation Gaussian noise image coding

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Optimization on unbounded solution space using dynamic genetic algorithms

Optimization on unbounded solution space using dynamic genet...

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International Joint Conference on Neural Networks (IJCNN)

作者： Chi Kin Chow Hung Tat Tsui Tong Lee Computer Vision and Image Processing Laboratory Department of Electronic Engineering Chinese University of Hong Kong Sha Tin Hong Kong China

ISBN: (纸本)0780370449

The problem of applying genetic algorithm (GA) to solve an optimization problem over an unbounded solution space is addressed. We propose to first transform the possible range of each parameter in the chromosome to a finite range with a nonlinear mapping such that the search on unbounded solution space becomes a search for high precision solution in a finite range. Modifications on the GA have been found necessary after such nonlinear mapping. As a result, a new GA with dynamic mutation range that facilitates coarse-refine search has been developed.

关键词： Genetic algorithms Biological cells Optimization methods Genetic mutations computer vision image processing Laboratories Genetic engineering Chromosome mapping Dynamic range

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Linear programming concept visualization

Linear programming concept visualization

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International Conference on Information Visualisation (IV)

作者： J.P. Charalambos E. Izquierdo Image Processing and Computer Graphics Research Laboratory Department of Computer Sciences National University Colombia Department of Electronic Engineering Queen Mary University of London London UK

A visualization scheme as a tool to find the solution of any 3D-linear programming problem is introduced. The presented approach is highly suitable to draw and visualize interactively the feasible region of a given 3D-linear programming problem. It can be used for a better understanding of the solution process when different methods devoted to solve the underlying problem are applied, e.g., the simplex method. The proposed technique comprises sensitive analysis during the solution process and interactive visualization of the feasible region. The analysis leading to the introduced schema also shows that the design of an appropriate and simple vertex representation is crucial to manage any order of degeneracy. To deal with this paradigm and to some extent to formalize it, the concept of adjacency invariance is introduced. Several experiments have been conducted to test and assess the performance of the introduced concepts and techniques.

关键词： Linear programming Visualization Testing computer graphics image processing Appropriate technology Stress Algorithm design and analysis

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