We are now developing a brain computer with algorithm acquisition function, where a two-level structure is introduced to connect pattern with (meta-)symbol, because we know how to realize algorithm acquisition on symb...
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ISBN:
(纸本)0819431184
We are now developing a brain computer with algorithm acquisition function, where a two-level structure is introduced to connect pattern with (meta-)symbol, because we know how to realize algorithm acquisition on symbols. At Level 1 we use a conventional learning method on neuralnetworks, but, at level 2;we develop a new learning algorithm AST (Abstract State Transition algorithm), where an automaton-like algorithm with a neural, network learning is introduced. This is enough powerful to realize an automatic algorithm acquisition. We will state a two-level structure and the AST learning algorithm. We focus on real-time image understanding which is a realization of human brain with eyes. We will summarize the features of our developing artificial brain system as follows;I) System, for Meta-Symbol as well as Pattern. 2) Architecture with Algorithm Acquisition Function. 3) Cognitive Memory Model as well as Biological Memory Model. To realize an artificial memory model to satisfy the features of 1)-3), we introduce a two-level architecture, where the Meta-Symbol is introduced at Level 2 while the Pattern is used for Level 1 as usual.
Vector Quantization (VQ) is a well known technique for signal compression and codification. In this paper we propose the filtering of images based on the codebooks obtained from Vector Quantization design algorithms u...
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ISBN:
(纸本)3540660682
Vector Quantization (VQ) is a well known technique for signal compression and codification. In this paper we propose the filtering of images based on the codebooks obtained from Vector Quantization design algorithms under a Bayesian framework. The Bayesian VQ filter consists in the substitution of the image pixel by the central pixel of the codevector that encodes the pixel and its neighborhood. This process can be interpreted as a Maximum A Posteriori restoration based on the codebook estimated from the image. We apply the VQ filter to noise removal in images from micromagnetic resonance. We compare our approach with the more conventional approach of applying VQ compression as a noise removal filter. Some visual results show the improvement introduced by our approach.
Lots of researches have been focused in the denoising of images. However, not much satisfactory result have been reported on images with extremely high noise content. The computations needed for processing were also t...
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Lots of researches have been focused in the denoising of images. However, not much satisfactory result have been reported on images with extremely high noise content. The computations needed for processing were also too tremendous for real time applications. A new approach is proposed in this paper for images with noise content as high as power 500 with reasonable amount of computations. Experiments show that the processing results by the proposed approach over the Wiener filter are better in the quality of the processed image and also less in computations involved.
In this paper we describe some of the most important types of neuralnetworks applied in biomedical imageprocessing. The networks described are variations of well-known architectures but are including image-relevant ...
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In this paper we describe some of the most important types of neuralnetworks applied in biomedical imageprocessing. The networks described are variations of well-known architectures but are including image-relevant features in their structure. Convolutional neuralnetworks, modified Hopfield networks, regularization networks and nonlinear principal component analysis neuralnetworks are successfully applied in biomedical image classification, restoration and compression.
Multi-valued and universal binary neurons (MVN and UBN) are the neuralprocessing elements with complex-valued wei^ts and high fimctionality. It is possible to implement an arbitrary mapping described by partial-defin...
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The line grain, data-driven parallelism shown by neural models as the Boltzmann machine cannot be implemented in an entirely efficient way either in general-purpose multicomputers or in networks of computers, which ar...
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ISBN:
(纸本)3540660682
The line grain, data-driven parallelism shown by neural models as the Boltzmann machine cannot be implemented in an entirely efficient way either in general-purpose multicomputers or in networks of computers, which are nowadays the most common parallel computer architectures. In this paper we present a parallel implementation of a modified Boltzmann machine where the processors, with disjoint subsets of neurons allocated, asynchronously compute the evolution of their neurons by using values that might not be updated for the remaining neurons, thus reducing interprocessor communication requirements. An evolutionary algorithm is used to learn the rules that allow the processors to cooperate by Interchanging the local optima that they find while concurrently exploring different zones of the Boltzmann machine state space. Thus, the way the processors interact changes dynamically during execution of the algorithm, adapted to the problem at hand. Good figures for speedup with respect to the Boltzmann machine computation in a uniprocessor computer have been experimentally obtained.
Detection of contours in biomedical images is quite often an a priori step to quantification. Using computer facilities, it is now straightforward for a medical expert to draw boundaries around regions of interest (RO...
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ISBN:
(纸本)0819431184
Detection of contours in biomedical images is quite often an a priori step to quantification. Using computer facilities, it is now straightforward for a medical expert to draw boundaries around regions of interest (ROI). However, accuracy of drawing is an issue, which is rarely addressed although it may be a crucial point when for example one looks for local evolution of boundaries on series of images. The aim of our study is to correlate the local accuracy of experts' outlines with local features of the underlying image to allow meaningful comparisons of boundaries. Local variability of experts' outlines has been characterized by deriving a Set of distances between outlines repeatedly drawn on the same image. Local features of underlying images were extracted from 64x64 pixel windows. We have used a two-stage neural network approach (a succession of a GHA unsupervised and a retropropagation supervised neuralnetworks) in order to deal with complexity of data within windows and to correlate their features with local variability of outlines. Our method has been applied to the quantification of the progression of the Cytomegalovirus (CMV) infection as observed from a series of retinal angiograms in patients with AIDS. Reconstruction of new windows from the set of primitives obtained from the GHA network shows that the method preserves desired features. Accuracy of the border of infection is properly predicted and allows to generate confidence envelope around every hand-outlined.
Using a recent algorithm for non linear mapping, Curvilinear Component Analysis, we show through three applications how a priori knowledge can be introduced in the CCA framework, and we translate this knowledge in ter...
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In this paper, we propose a method of Shape from Texture. There are some major approaches to estimate 3-D shape. Our method uses the peak frequency for feature of texture,:as it is known to be used in human perception...
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ISBN:
(纸本)0819431184
In this paper, we propose a method of Shape from Texture. There are some major approaches to estimate 3-D shape. Our method uses the peak frequency for feature of texture,:as it is known to be used in human perception. Our proposed system is composed of 1-D and 2-D system. The 1-D system estimates a local peak frequency is composed of two steps. First is the feature extraction step. For extracting the feature of image, we use 16 Gabor filters with successive Gauss filters as post smoothing filter. Second is the estimation of a local peak frequency step with neural network. A local peak frequency is estimated from the neural network. We use a three layers network whose parameters are detemined by Back Propagation network training. By using neural network, the performance of 16 Gabor filters is demonstrated as efficient as that with more filters. We use this algorithm for separate orientation channel. 2-D system inhibits estimated local peak frequency of 4 orientations in 1-D System. And we estimate 3-D shape from the ratio of local peak frequencies in 2-D. This system is not effective for the estimation near object edge. Then, we use Edge information for improving the method.
Performances evaluation of imageprocessing intermediate results in video based surveillance systems is extremely important due to the variety of approaches to this task. In this paper, an approach based on the use of...
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Performances evaluation of imageprocessing intermediate results in video based surveillance systems is extremely important due to the variety of approaches to this task. In this paper, an approach based on the use of Receiver Operating Characteristics (ROC) curves in order to evaluate the performances of a vision complex system for surveillance purposes is presented. ROC curves have already been used in other research fields as comparison of edge detection algorithms or evaluation of artificialneuralnetworks: in this case they are used in order to compare different parameters selections within a system for the localization of moving objects. Presented results show the possibility of using ROC curves as a mean for evaluation and comparison of video-based surveillance systems.
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