In this paper some of the commonly used features for texture classification based on co- occurrence statistics are studied. First, the classification capabilities of individual features in classifying among a small an...
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ISBN:
(纸本)0819409391
In this paper some of the commonly used features for texture classification based on co- occurrence statistics are studied. First, the classification capabilities of individual features in classifying among a small and a large number of texture images are evaluated. Then, the capabilities of different combinations of texture features are examined in order to establish a reduced set of features for maximum performance. An artificial neural network is used to test the suitability of promising feature groups for texture classification. It is shown that the features considered may be broadly divided into two groups in terms of their classification performance. It is also shown that with a judicious choice of features and a well trained neural network classifier, high recognition rates can be achieved.
We define genetic annealing as simulated annealing applied to a population of several solutions when candidates are generated from more than one (parent) solution at a time. We show that such genetic annealing algorit...
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ISBN:
(纸本)0819409391
We define genetic annealing as simulated annealing applied to a population of several solutions when candidates are generated from more than one (parent) solution at a time. We show that such genetic annealing algorithms can inherit the convergence properties of simulated annealing. We present two examples, one that generates each candidate by crossing pairs of parents and a second that generates each candidate from the entire population. We experimentally apply these two extreme versions of genetic annealing to a problem in vector quantization.
Good image segmentation can be achieved by finding the optimum solution to an appropriate energy function. A Hopfield neural network has been shown to solve complex optimization problems fast, but it only guarantees c...
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ISBN:
(纸本)0819409391
Good image segmentation can be achieved by finding the optimum solution to an appropriate energy function. A Hopfield neural network has been shown to solve complex optimization problems fast, but it only guarantees convergence to a local minimum of the optimization function. Alternatively, mean field annealing has been shown to reach the global or the nearly global optimum solution when solving optimization problems. Furthermore, it has been shown that there is a relationship between a Hopfield neural network and mean field annealing. In this paper, we combine the advantages of the Hopfield neural network and the mean field annealing algorithm and propose using an annealed Hopfield neural network to achieve good image segmentation fast. Here, we are concerned not only with identifying the segmented regions, but also finding a good approximation to the average gray level for each segment. A potential application is segmentation-based image coding. This approach is expected to find the global or nearly global solution fast using an annealing schedule for the neural gains. A weak continuity constraints approach is used to define the appropriate optimization function. The simulation results for segmenting noisy images are very encouraging. Smooth regions were accurately maintained and boundaries were detected correctly.
In this paper the neural network concept is studied, as a non-linear dynamic system, for predicting spatiotemporal patterns. The relative behavior of two back-error propagation neural network (BPNN) configurations is ...
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ISBN:
(纸本)0819409391
In this paper the neural network concept is studied, as a non-linear dynamic system, for predicting spatiotemporal patterns. The relative behavior of two back-error propagation neural network (BPNN) configurations is investigated in the context of real world data from geostationary meteorological satellite (GOES) images. One of them explores only temporal information, the other one takes into account spatial-contextual pattern aspects. The results demonstrate that neural networks are a useful tool for time series prediction of spatial patterns. It means that with certain accuracy future states of a spatial phenomena can be generated before the satellite captures them in its next imaging.
A novel weighted outer-product learning (WOPL) scheme for associative memory neural networks (AMNNs) is presented. In the scheme, each fundamental memory is allocated a learning weight to direct its correct recall. Bo...
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ISBN:
(纸本)0819409391
A novel weighted outer-product learning (WOPL) scheme for associative memory neural networks (AMNNs) is presented. In the scheme, each fundamental memory is allocated a learning weight to direct its correct recall. Both the Hopfield and multiple training models are instances of the WOPL model with certain sets of learning weights. A necessary condition of choosing learning weights for the convergence property of the WOPL model is obtained through neural dynamics. A criterion for choosing learning weights for correct associative recalls of the fundamental memories is proposed. In this paper, an important parameter called signal to noise ratio gain (SNRG) is devised, and it is found out empirically that SNRGs have their own threshold values which means that any fundamental memory can be correctly recalled when its corresponding SNRG is greater than or equal to its threshold value. Furthermore, a theorem is given and some theoretical results on the conditions of SNRGs and learning weights for good associative recall performance of the WOPL model are accordingly obtained. In principle, when all SNRGs or learning weights chosen satisfy the theoretically obtained conditions, the asymptotic storage capacity of the WOPL model will grow at the greatest rate under certain known stochastic meaning for AMNNs, and thus the WOPL model can achieve correct recalls for all fundamental memories. The representative computer simulations confirm the criterion and theoretical analysis.
Dempster-Shafer's theory of evidence is a generalization of Bayes reasoning that allows multiple information sources with varying levels of belief to contribute to probabilistic decisions. We present an algorithm ...
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ISBN:
(纸本)0819409391
Dempster-Shafer's theory of evidence is a generalization of Bayes reasoning that allows multiple information sources with varying levels of belief to contribute to probabilistic decisions. We present an algorithm that performs pixel-level segmentation based upon the Dempster-Shafer theory of evidence. The algorithm fuses image data from the multichannels of color spectra. Dempster-Shafer reasoning is used to drive the evidence accumulation process for pixel level segmentation of color scenes. Experiments are presented that use spectral information from the RGB and HSI color models to segment a color image with Dempster-Shafer reasoning. These experiments begin to point out the utility and pitfalls of using Dempster-Shafer reasoning for segmenting color images.
Integration of information from multiple sources has been one of the key steps to the success of general vision systems. It is also an essential problem to the development of color image understanding algorithms that ...
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ISBN:
(纸本)0819409391
Integration of information from multiple sources has been one of the key steps to the success of general vision systems. It is also an essential problem to the development of color image understanding algorithms that make full use of the multichannel color data for object recognition. This paper presents a feature integration system characterized by a hybrid combination of a statistic-based reasoning technique and a symbolic logic-based inference method. A competitive evidence enhancement scheme is used in the process to fuse information from multiple sources. The scheme expands the Dempster-Shafer's function of combination and improves the reliability of the object recognition. When applied to integrate the object features extracted from the multiple spectra of the color images, the system alleviates the drawback of traditional Baysian classification system.
Iterated transformation theory (ITT), also known as fractal coding, is a relatively new block compression method which removes redundancies between different scale representations of the uncompressed signal. In ITT co...
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ISBN:
(纸本)0819409391
Iterated transformation theory (ITT), also known as fractal coding, is a relatively new block compression method which removes redundancies between different scale representations of the uncompressed signal. In ITT coding we are looking for a piecewise continuous mapping from the space of all images with the same support onto itself which has a close approximation of the desired image as a unique fixed point. The mapping is then the code for the image, and for decoding we iterate the mapping on any initial image, orders of magnitude faster than encoding. We have reduced the computational load of finding the piecewise continuous transformation by using a self-organizing feature map (SOFM) artificial neural network which finds similar features in different resolution representations of the image. The patterns are mapped onto a two-dimensional array of formal neurons forming a code book similar to vector quantization (VQ) coding. We use the (SOFM) ordering properties by searching for mapping not only to the best feature match neuron but also to its neighbors in the network. In this paper we describe the ITT-SOFM algorithm and its software implementation with application to image coding of still gray images. Computer simulations show compression results comparable to or better than state-of-the-art VQ coders, and computational complexity better than most of the well known clustering algorithms.
Feedforward networks are used extensively in practice to learn static mappings between related sets of variables. These networks are difficult to analyze, however, both because of their nonlinearity and their complex ...
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ISBN:
(纸本)0819409391
Feedforward networks are used extensively in practice to learn static mappings between related sets of variables. These networks are difficult to analyze, however, both because of their nonlinearity and their complex interconnection structure. In the absence of the nonlinearity, linear algebra could provide considerable insight into the behavior of these networks, significantly beyond that possible from a detailed analysis of individual neurons. Such insights would be extremely valuable since the power of neural networks arises from their large-scale connectivity, rather than the inherent computational capacity of the individual neurons. This paper proposes algebraic category theory as the basis for obtaining such global insights for feedforward networks in spite of their nonlinearity.
This paper describes a multispectral image classification technique. This technique involves two steps. First, we describe the underlying distribution of the pixel intensity vectors for the entire scene as a mixture o...
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ISBN:
(纸本)0819409391
This paper describes a multispectral image classification technique. This technique involves two steps. First, we describe the underlying distribution of the pixel intensity vectors for the entire scene as a mixture of multivariate Gaussian distributions. We then use this mixture decomposition and a small number of labeled pixels to estimate the proportion of a mixture component that is comprised of a certain class, which enables us to use a Bayes-type decision rule to classify each pixel in the scene. Results of applying this technique to three-band SPOT data are presented. Comparisons with results obtained from a maximum likelihood classifier are also presented.
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