This paper proposes an unsupervised learning algorithm for linear neural network (LNN), two activity measurements are designed to classify the image subblocks into four categories. In order to improve the performance ...
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ISBN:
(纸本)0819409391
This paper proposes an unsupervised learning algorithm for linear neural network (LNN), two activity measurements are designed to classify the image subblocks into four categories. In order to improve the performance of LNN, an adaptive scheme is presented. The simulation results show that better reconstructed image quality is achieved than previous algorithms.
This work investigates the application of a stochastic search technique, evolutionary programming, for developing self-organizing neural networks. The chosen stochastic search method is capable of simultaneously evolv...
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ISBN:
(纸本)0819409391
This work investigates the application of a stochastic search technique, evolutionary programming, for developing self-organizing neural networks. The chosen stochastic search method is capable of simultaneously evolving both network architecture and weights. The number of synapses and neurons are incorporated into an objective function so that network parameter optimization is done with respect to computational costs as well as mean pattern error. Experiments are conducted using feedforward networks for simple binary mapping problems.
The higher order cumulants and their Fourier transforms, polyspectra, are used in order to achieve a number of objects which may not be possible to obtain using second order statistics. In this paper, we study differe...
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ISBN:
(纸本)0819409391
The higher order cumulants and their Fourier transforms, polyspectra, are used in order to achieve a number of objects which may not be possible to obtain using second order statistics. In this paper, we study different approaches to estimate the bispectrum and apply the result to the image reconstruction and communication signal identification. One of the key advantages of using cumulants in the signalprocessing are is that cumulants are blind to all kinds of Gaussian processes. Thus, when a cumulants method is used on non-Gaussian signals polluted by additive Gaussian noise, it will improve the signal/noise ratio.
Hierarchically organized neural networks are well suited for visual information processing. These models offer a way to cope with the complexity of vision. We identify strong relationships between hierarchical neural ...
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ISBN:
(纸本)0819409391
Hierarchically organized neural networks are well suited for visual information processing. These models offer a way to cope with the complexity of vision. We identify strong relationships between hierarchical neural networks and image pyramids. However, we also show that if one has the freedom to choose the input patterns, these neural networks are not intrinsically shift invariant. In order to circumvent this problem we propose a new neural network architecture called `neural Networks in image Pyramids.' We use hierarchical neural networks with local connectivity (image pyramids) as stem networks. These networks generate hypotheses about the expected image content. These hypotheses are checked by small neural network modules which are used selectively on parts of the image. We give an example demonstrating the solution of the shift variance problem. Finally, we outline directions of further research.
After the stochastic simulated annealing technique was applied in the field of imageprocessing, there have been many research reports on the Markov random field based imageprocessing. These MRF-based edge-preserving...
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ISBN:
(纸本)0819409391
After the stochastic simulated annealing technique was applied in the field of imageprocessing, there have been many research reports on the Markov random field based imageprocessing. These MRF-based edge-preserving smoothing techniques showed good results in the field of restoration, reconstruction, edge detection, and segmentation of the images, however, they have common drawbacks. First, those methods do not work well for smoothing of the nonstationary or signal-dependent noise. In real world images, the noises are often nonstationary and signal-dependent. Second, those edge-preserving smoothing techniques employ implicit or explicit thresholds to determine the existence of the edges, and they use fixed single thresholds throughout the entire image. As a result of these drawbacks, small features in the area of low noise variance are lost or blurred in order to restore the features in the high variance area. In order to cure these problems, we need an adaptive edge-preserving smoothing method which can be applied to nonstationary or signal-dependent noise with adaptive thresholding. The adaptive mean field annealing is an adaptive version of MFA, which fulfills this purpose by taking advantage of the local nature of the MRF and the fact that nonstationary or signal-dependent noise can be approximated by locally stationary additive Gaussian noise. In AMFA, the a priori information about the noise is not necessary and, hence, the difficulty of estimating the parameters is greatly reduced.
Performance measures are derived for data-adaptive hypothesis testing by systems trained on stochastic data. The measures consist of the averaged performance of the system over the ensemble of training sets. The train...
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ISBN:
(纸本)0819409391
Performance measures are derived for data-adaptive hypothesis testing by systems trained on stochastic data. The measures consist of the averaged performance of the system over the ensemble of training sets. The training set-based measures are contrasted with maximum aposteriori probability (MAP) test measures. It is shown that the training set-based and MAP test probabilities are equal if the training set is proportioned according to the prior probabilities of the hypotheses. Applications of training set-based measures are suggested for neural net and training set design.
A novel method of color human face image recognition is presented in this paper. First, an input color face image is transformed into a monochrome image which contains enough useful information for recognition. This m...
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ISBN:
(纸本)0819409391
A novel method of color human face image recognition is presented in this paper. First, an input color face image is transformed into a monochrome image which contains enough useful information for recognition. This monochrome image is then transformed into a standard image. The face recognition is completed via classification of the projective feature vectors of the standard image by a minimum distance classifier. Experimental results showed that the method is effective.
Hebbian learning law plays a very important role in the feedforward learning of neural networks. In multidimensional image space, particularly in vision, the asymmetric multidimensional Hebbian learning law can perfor...
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ISBN:
(纸本)0819409391
Hebbian learning law plays a very important role in the feedforward learning of neural networks. In multidimensional image space, particularly in vision, the asymmetric multidimensional Hebbian learning law can perform principal component feature extraction, thus providing high dimensional feature analysis and feature separation. In this paper, we verified this principle with modified Hebbian learning when applied to Fukushima's neocognitron visual recognition architecture.
Increasingly huge amounts of digital data from a wide range of sources such as B-ISDN services, satellite transmission of photographs, and police database of human face images are being transmitted and stored. Therefo...
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ISBN:
(纸本)0819409391
Increasingly huge amounts of digital data from a wide range of sources such as B-ISDN services, satellite transmission of photographs, and police database of human face images are being transmitted and stored. Therefore, both transmission channel capacity and disk space are limited. For some advanced techniques, such as multi-media terminal and HDTV etc., the problems are even more apparent. Based on this it is important that efficient image compression algorithms are used in order to reduce the transmission capacity and storage space. In this paper, a scheme of image data compression with an adaptive BP neural network is presented. The data compression property of mapping original image to a feature space of reduced dimensionality is utilized. images are divided as a set of 8 × 8 sub-image blocks which apply to a three layer BP neural network as inputs. It is shown from computer simulation that the results are better than Sonehara, et al.
This paper builds upon earlier work on the wave expansion neural network (WENN) which is a neural network capable of implementing wavefront expansion operations useful for developing potential fields for path planning...
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ISBN:
(纸本)0819409391
This paper builds upon earlier work on the wave expansion neural network (WENN) which is a neural network capable of implementing wavefront expansion operations useful for developing potential fields for path planning. The discretized operational space or configuration space (C-Space) is mapped on to the WENN neural field which subsequently develops the artificial potential field over the C-space. The WENN has been applied to develop a simple attractive potential field and a repulsive potential field over two dimensional workspaces.
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