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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing II"

共 2648 条记录，以下是2231-2240 订阅

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Design of an adaptive genetic learning neural network system for image compression 2

Design of an adaptive genetic learning neural network system...

引用

conference on applications of artificial neural networks in image processing ii

作者： Jiang, J Butler, D Loughborough Univ. (United Kingdom) Bolton Institute (United Kingdom)

ISBN: (纸本)0819424412

In this paper, we describe a genetic learning neural network system to vector quantize images directly to achieve data compression. The genetic learning algorithm is designed to have two levels: One is at the level of code words in which each neural network is updated through reproduction every time an input vector is processed. The other is at the level of code-books in which five neural networks are included in the gene pool. Extensive experiments on a group of image samples show that the genetic algorithm outperforms other vector quantization algorithms which include competitive learning, frequency sensitive learning and LBG.

关键词： genetic algorithm neural networks vector quantization image compression and processing

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Predictive vector quantization using neural networks 2

Predictive vector quantization using neural networks

引用

conference on applications of artificial neural networks in image processing ii

作者： Hashemi, MR Yeap, TH Panchanathan, S UNIV OTTAWA DEPT ELECT ENGNVISUAL COMP & COMMUN LABOTTAWAON K1N 6N5CANADA

ISBN: (纸本)0819424412

In this paper we propose a new scalable predictive vector quantization (PVQ) technique for image and video compression, This technique has been implemented using neural networks. A Kohonen self organized feature may, is used to implement the vector quantizer, while a multilayer perceptron implements the predictor. Simulation results demonstrate that the proposed technique provides a 5-10% improvement in coding performance over the existing neural networks based PVQ techniques.

关键词： predictive vector quantization neural networks image compression multimedia

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image segmentation by multigrid MRF and perceptual optimization 2

Image segmentation by multigrid MRF and perceptual optimizat...

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conference on applications of artificial neural networks in image processing ii

作者： Zhang, J Wang, D Liu, J Univ. of Wisconsin/Milwaukee (United States)

ISBN: (纸本)0819424412

This paper describes a Markov random field (MRF) approach to image segmentation. Unlike most previous MRF techniques, which are based on pixel-classification, this approach groups pixels that are similar. This removes the need to know the number of image classes. Mean field theory and multigrid processing are used in the subsequent optimization to find a good segmentation and to alleviate local minimum problems. Variations of the MRF approach are investigated by incorporating features/schemes motivated by characteristics of the human vision system (HVS). Preliminary results are promising and indicate that multi-grid and HVS based features/schemes can significantly improve segmentation results.

关键词： image segmentation Magnetorheological finishing image filtering Human vision and color perception image compression Eye models Probability theory Digital filtering Computer science Electrical engineering

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Automatic target recognition using a modular neural network with directional variance features 2

Automatic target recognition using a modular neural network ...

引用

conference on applications of artificial neural networks in image processing ii

作者： Wang, LC Der, S Rizvi, SA Nasrabadi, NM SUNY/Buffalo (United States) U.S. Army Research Lab. (United States) CUNY/College of Staten Island (United States)

ISBN: (纸本)0819424412

A modular neural network classifier has been applied to the problem of automatic target recognition (ATR) using forward-looking infrared (FLIR) imagery. This modular network classifier consists of several neural networks (expert networks) for classification. Each expert network in the modular network classifier receives distinct inputs from features extracted from only a local region of a target, known as a receptive field, and is trained independently from other expert networks. The classification decisions of the individual expert networks are combined to determine the final classification. Our experiments show that this modular network classifier is superior to a fully connected neural network classifier in terms of complexity (number of weights to be learned) and performance (probability of correct classification). The proposed classifier shows a high noise immunity to clutter or target obscuration due to the independence of the individual neural networks in the modular network. Performance of the proposed classifier is further improved by the use of multi-resolution features and by the introduction of a higher level neural network on the top of expert networks, a method known as stacked generalization.

关键词： forward-looking infrared (FLIR) automatic target recognition (ATR) neural network multi-resolution feature extraction stacked generalization

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Block predictive image coder of neural network in multiresolution domain 2

Block predictive image coder of neural network in multiresol...

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conference on applications of artificial neural networks in image processing ii

作者： Lou, SQ Huangpu, K Zhou, LZ Wang, JW Xu, GM National Univ. of Defense Technology (China)

ISBN: (纸本)0819424412

The redundancy of the multiresolution representation has been clearly demonstrated in the case of fractal images, but has not been fully recognized and exploited for general images, This paper presents a new image coder in which the similarity among blocks of different subbands is exploited by block prediction based on neural network. After a pyramid subband decomposition, the detail subbands are partitioned into a set of uniform non-overlapping blocks. In order to speed up the coding procedure and improve the coding efficiency, a new classifying criteria is presented, the blocks are classified into two sets : the simple block set and the edge block set. In our proposed method, the edge blocks are predicted from blocks in lower scale subband with sane orientation through neural network. The simple blocks and predictive edge error blocks are coded with arithmetic coder simulation results show that the method presented in this paper is a promising coding technique which is worth for us to do further research.

关键词： image coding wavelet transform neural network

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Automatic feature extraction in neural network noniterative learning 2

Automatic feature extraction in neural network noniterative ...

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conference on applications of artificial neural networks in image processing ii

作者： Hu, CLJ Southern Illinois Univ./Carbondale (United States)

ISBN: (纸本)0819424412

It is proved analytically, whenever the input-output mapping of a one-layered hard-limited perceptron satisfies a positive, linear independency (PLI) condition, the connection matrix A to meet this mapping can be obtained noniteratively in one step from an algebraic matrix equation containing an NxM input matrix U. Each column of U is a given standard pattern vector, and there are M standard patterns to be classified It is also analytically proved that sorting out all nonsingular sub-matrices U-k in U can be used as an automatic feature extraction process in this noniterative-learning system. This paper reports the theoretical derivation and the design and experiments of a superfast-learning, optimally-robust, neural network pattern recognition system utilizing this novel feature extraction process. An unedited video movie showing the speed of learning and the robustness in recognition of this novel pattern recognition system will be demonstrated in life. Comparison to other neural network pattern recognition systems will be discussed.

关键词： Feature extraction neural networks Pattern recognition Machine learning Analog electronics Brain mapping Matrices Video Electrical engineering Neurons

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Adaptive regularization in image restoration using a model-based neural network 2

Adaptive regularization in image restoration using a model-b...

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conference on applications of artificial neural networks in image processing ii

作者： Wong, HS Guan, L Univ. of Sydney (Australia) Univ. of Sydney (Canada)

ISBN: (纸本)0819424412

The determination of the regularization parameter is an important sub-problem in optimizing the performances of image restoration systems. The parameter controls the relative weightings of the data-conformance and model-conformance terms in the restoration cost function. A small parameter value would lead to noisy appearances in the smooth image regions due to over-emphasis of the data term, while a large parameter results in blurring of the textured regions due to dominance of the model term. Based on the principle of adopting small parameter values for the highly textured regions for detail emphasis while using large values for noise suppression in the smooth regions, a spatially adaptive regularization scheme was derived in this paper. An initial segmentation based on the local image activity was performed and a distinct regularization parameter was associated with each segmented component. The regional value was estimated by viewing the parameter as a set of learnable neuronal weights in a Model-Based neural network. A stochastic gradient descent algorithm based on the regional spatial characteristics and specific functional form of the neuronal weights was derived to optimize the regional parameter values. The efficacy of the algorithm was demonstrated by our observation of the emergence of small parameter values in textured regions and large values in smooth regions.

关键词： image restoration neural networks regularization

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Backpropagation neural network for adaptive color image segmentation. 2

Backpropagation neural network for adaptive color image segm...

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conference on applications of artificial neural networks in image processing ii

作者： Krjukov, SN Semenkova, TO Pavlova, VA Arnt, BI S.I. Vavilov State Optical Institute (Russia)

ISBN: (纸本)0819424412

A practical approach to continuos-tone color image segmentation is proposed. Unlike traditional algorithms of image segmentation which tend to use threshold methods we intend to show how neural network technique can be successfully applied to this problem. We used a Bacpropagation network architecture in this work. It was assumed that each image pixel has its own color, which is somehow correlated with those of the nearest neighborhood. To describe the color properties of certain neighborhood we suggested nine component feature vector for every image pixel. This set of feature components is applied to the network input neurons. By this means,every image pixel is described by the following values R, G and B (color Intensities), Mr, Mg and Mb (averages of intensities of the nearest neighborhood), sigma(r), sigma(g)land sigma(b) (.r, .m, s, deviations of color intensities). To estimate the algorithm efficiency the scalar criterion was proposed. It was shown by the results of comparative experiment that neural segmentation provides more efficiency then that of traditional, using threshold methods.

关键词： segmentation backpropagation neural network feature vector threshold methods

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Compression of digital mammograms using wavelets and learning vector quantization 2

Compression of digital mammograms using wavelets and learnin...

引用

conference on applications of artificial neural networks in image processing ii

作者： Wang, TC Karayiannis, NB U.S. Robotics (United States) Univ. of Houston (United States)

ISBN: (纸本)0819424412

This paper evaluates the performance of a system which compresses digital mammograms, In digital mammograms, important diagnostic features such as the microcalcifications appear in small clusters of few pixels with relatively high intensity compared with their neighboring pixels, These image features can be preserved in a compression system that employs a suitable image transform which can localize the signal characteristics in the original and the transform domain. image compression is achieved by first decomposing the mammograms into different subimages carrying different frequencies, and then employing vector quantization to encode these subimages. Multiresolution codebooks are designed by the Linde-Buzo-Gray (LBG) algorithm and a family of fuzzy algorithms for learning vector quantization (FALVQ). The main advantage of the proposed approach is the design of separate multiresolution codebooks for different subbands of the decomposed image that carry different orientation and frequency information, The experimental results confirm the viability of the proposed compression scheme on digital mammograms.

关键词： Wavelets image compression image filtering Mammography Prototyping Quantization Filtering (signal processing) Electronic filtering Optical filters Algorithm development

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image compression using a self-organized neural network 2

Image compression using a self-organized neural network

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conference on applications of artificial neural networks in image processing ii

作者： Ji, Q Univ. of Washington (United States)

ISBN: (纸本)0819424412

In the research described by this paper, we implemented and evaluated a linear self-organized feedforward neural network for image compression. Based on the Generalized Hebbian Learning Algorithm (GHA), the neural network extracts the principle components from the auto-correlation matrix of the input images. To do so, an image is first divided into mutually exclusive square blocks of size m x m. Each block represents a feature vector of m(2) dimension in the feature space. The input dimension of the neural net is therefore m(2) and the output dimension is m. Training based on GHA for each block then yields a weight matrix with dimension of m x m(2), rows of which are the eigenvectors of the auto-correlation matrix of the input image block, Projection of each image block onto the extracted eigenvectors yields m coefficients for each block. image compression is then accomplished by quantizing and coding the coefficients for each block. To evaluate the performance of the neural network, two experiments were conducted using standard IEEE images. First, the neural net was implemented to compress images at different bit rates using different block sizes, Second, to test the neural network's generalization capability, the sets of principle components extracted from one image was used for compressing different but statistically similar images. The evaluation, based on both visual inspection and statistical measures (NMSE and SNR) of the reconstructed images, demonstrates that the network can yield satisfactory image compression performance and possesses a good generalization capability.

关键词： image compression neural networks

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