The interpretation of neural network behavior is of particular interest in neural network research. Visualization methods provide the necessary means to simultaneously analyze the huge amount of information hidden in ...
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The interpretation of neural network behavior is of particular interest in neural network research. Visualization methods provide the necessary means to simultaneously analyze the huge amount of information hidden in the network. The authors propose a framework for visualization methods suited for feed forward neural networks. The basic idea is to use the spatial information available outside the network to arrange the data to be visualized (weights, activations of units) in the spatial domain of the display. Several examples which illustrate the proposed framework are presented.< >
Compared with the richness in detail of images generated by iterated function systems, the memory requirement of them is very low. Displaying these images is very time consuming, and currently there is no method publi...
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Compared with the richness in detail of images generated by iterated function systems, the memory requirement of them is very low. Displaying these images is very time consuming, and currently there is no method published to derive an iterated function system directly from a given image. This is known as the inverse problem. The authors introduce discrete transformations and direct computation of a discrete attractor with deterministic noniterative algorithms. This results in an essential saving of time. Furthermore they present a solution of the inverse problem in the one dimensional discrete space.< >
Computer vision and image understanding processes are not very robust; small changes in exposure parameters or in internal parameters of algorithms can lead to significantly different results. A combination (fusion) o...
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Computer vision and image understanding processes are not very robust; small changes in exposure parameters or in internal parameters of algorithms can lead to significantly different results. A combination (fusion) of these results is profitable. The authors introduce an extended fusion concept dealing with different sources of information at external (world, scene, image) and internal (image description, scene description) levels and define the process of fusion. Each level requires its own procedure of quality measure and information fusion in order to yield a combination of components from several sources. Related work in the field is reviewed. Examples from the authors' own work cover remote sensing (improvement of classification results by fusion at the image level), medical imageprocessing of ocular fundus images (automatic control point selection by fusion at the image description level) and the interpretation of Billard scenes (object identification by fusion at the scene description level).< >
Scanning Auger microscopy (SAM) and AES investigations of the segregation of non-metal impurity atoms (sulphur, nitrogen, carbon and phosphorus) in polycrystalline high-purity (>99.99%) α-iron samples were perform...
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A theoretical framework based on attributed elementary programmed graph grammars (GRAPE grammars) that turns out to be equally well-suited for describing network dynamics, weight learning algorithms, and topology lear...
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A theoretical framework based on attributed elementary programmed graph grammars (GRAPE grammars) that turns out to be equally well-suited for describing network dynamics, weight learning algorithms, and topology learning algorithms is proposed. It is shown how GRAPE grammars can be used to model neural networks. Special emphasis is placed on topology learning. It is concluded that GRAPE grammars offer a great potential for neural networks, giving the possibility of a common language suited for all kinds of neural networks.< >
Based on the assumption that most probability densities in real life can be approximated by a mixture of Gaussian densities, we propose here a three-layer adaptive network with each neuron in the lower hidden layer re...
Based on the assumption that most probability densities in real life can be approximated by a mixture of Gaussian densities, we propose here a three-layer adaptive network with each neuron in the lower hidden layer representing a Gaussian basis function (covariance matrix equal to where I is a unit matrix) to estimate various probability densities and serve as a Bayes classifier. The width of the basis function may be the same for all neurons in this layer or it may vary from one neuron to another. This paper investigates the effectiveness of the network for both cases and presents a localized learning algorithm to adjust the network parameters. The network was trained with artificial data derived from known mixtures of memoryless Gaussian sources as well as exponential and Gamma densities. The performance of the network as a pattern density estimator was measured in terms of the relative difference between the target probability density function (p.d.f.) which generates the training and testing data and the network output representing the estimation. Samples from two mixtures corresponding to two classes were used to test the network capability as a classifier by comparing its error rate against that of a Bayes classifier. Both one- and two-dimensional cases were explored. The successfulness of the network depended on how well the target p.d.f.’s were represented by the training samples, the number of hidden neurons employed in the network and how thoroughly the network was trained. It was also found that allowing each basis function to have an independent width had a predominant effect on the network performance.
An efficient discrete cosine transform technique using a new adaptive feature for bandwidth compression is described. Taking account of human visual characteristics and transform coefficient statistics, the higher act...
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An efficient discrete cosine transform technique using a new adaptive feature for bandwidth compression is described. Taking account of human visual characteristics and transform coefficient statistics, the higher activity region is further classified into four subclasses according to four proposed basic patterns, while the lower activity region is assigned to four subclasses according to its AC-energy (image activity) distribution. Computer simulations shows that the proposed adaptive coder exhibits a performance improvement of 1 dB or more over conventional adaptive coders at the same coding rates.< >
An improved method for shape from shading is presented. With introduction of the adaptive attenuated factors, the results of the initial iterations fall into the region of the solution values as much as possible. Simu...
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ISBN:
(纸本)0818608781
An improved method for shape from shading is presented. With introduction of the adaptive attenuated factors, the results of the initial iterations fall into the region of the solution values as much as possible. Simulated tests show that the method makes a notable improvement over the well-known approach proposed by K. Ikeuchi and B.K.P. Horn (1981), not only on the correctness of the solution but also on the speed of the convergence. The case of the actual object is also discussed. The result is satisfactory.< >
The Laplacian of Gaussian operator, Del /sup 2/G, is very important as an edge detector in the theory of computer vision. The bias of zero-crossing and output signal-to-noise-ratio of Del /sup 2/G under the models of ...
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ISBN:
(纸本)0818608781
The Laplacian of Gaussian operator, Del /sup 2/G, is very important as an edge detector in the theory of computer vision. The bias of zero-crossing and output signal-to-noise-ratio of Del /sup 2/G under the models of four typical kinds of edges corrupted by white noise are given, and these theoretical results are confirmed by experiments. The relations among bias of zero-crossing, output and input signal-to-noise-ratio and parameter sigma of Del /sup 2/G are presented.< >
The intention of this paper is to help bridging the gap between knowledge base and computer vision system. A knowledge-based vision system for identification of overlapping objects is presented. The authors place emph...
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ISBN:
(纸本)7800030393
The intention of this paper is to help bridging the gap between knowledge base and computer vision system. A knowledge-based vision system for identification of overlapping objects is presented. The authors place emphasis on the reasoning strategy based on knowledge base for recognizing of occluded workpieces to provide information with an education Robot. The experimental results are given and some problem are discussed.
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