The proceedings contain 17 papers. The topics discussed include: entropy-constrained learning vector quantization algorithms and their application in image compression;predictive vector quantization using neural netwo...
The proceedings contain 17 papers. The topics discussed include: entropy-constrained learning vector quantization algorithms and their application in image compression;predictive vector quantization using neuralnetworks;design of an adaptive genetic learning neural network system for image compression;binary image compression using identity mapping backpropagation neural network;block-predictive image coder of neural network in multiresolution domain;and comparison of ML parameter estimation and neural network classifier for texture classification.
Our skin is the hefty organ that envelops and shields body. It prevents us from numerous fatal and non fatal diseases. It is observed that due to bacteria or other causes of infection, skin faces certain minor or life...
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Inspired by the extensive signal processing capabilities of the human nervous system, neuromorphic artificial sensory systems have emerged as a pivotal technology in advancing brain-like computing for applications in ...
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Inspired by the extensive signal processing capabilities of the human nervous system, neuromorphic artificial sensory systems have emerged as a pivotal technology in advancing brain-like computing for applications in humanoid robotics, prosthetics, and wearable technologies. These systems mimic the functionalities of the central and peripheral nervous systems through the integration of sensory synaptic devices and neural network algorithms, enabling external stimuli to be converted into actionable electrical signals. This review delves into the intricate relationship between synaptic device technologies and neural network processing algorithms, highlighting their mutual influence on artificial intelligence capabilities. This study explores the latest advancements in artificial synaptic properties triggered by various stimuli, including optical, auditory, mechanical, and chemical inputs, and their subsequent processing through artificialneuralnetworks for applications in image recognition and multimodal pattern recognition. The discussion extends to the emulation of biological perception via artificial synapses and concludes with future perspectives and challenges in neuromorphic system development, emphasizing the need for a deeper understanding of neural network processing to innovate and refine these complex systems.
The analog CMOS circuit realization of cellular neuralnetworks with transconductance elements is presented. This realization can be easily adapted to various types of applications in imageprocessing just by choosing...
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The analog CMOS circuit realization of cellular neuralnetworks with transconductance elements is presented. This realization can be easily adapted to various types of applications in imageprocessing just by choosing the appropriate transconductance parameters according to the predetermined coefficients. The effectiveness of the designed circuits for connected component detection is shown by HSPICE simulations. For ''fixed function'' cellular neural network circuits the number of transistors are reduced further by using multi-input transconductance elements.
A controversial issue in the research of mathematics of intelligence has been that of the roles of a priori knowledge versus adaptive learning, After discussing mathematical difficulties of combining a priority with a...
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A controversial issue in the research of mathematics of intelligence has been that of the roles of a priori knowledge versus adaptive learning, After discussing mathematical difficulties of combining a priority with adaptivity encountered in the past, we introduce a concept of a model-based neural network, whose adaptive learning is based on a priori models. applications to target detection in SAR images are discussed, We briefly overview the SAR principles, derive relatively simple physics-based models of SAR signals, and describe model-based neuralnetworks that utilize these models. A number of real-world application examples are presented.
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...
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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 neuralnetworks 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.
In this paper we propose a new scalable predictive vector quantization (PVQ) technique for image and video compression, This technique has been implemented using neuralnetworks. A Kohonen self organized feature may, ...
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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 neuralnetworks. 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 neuralnetworks based PVQ techniques.
Given that neuralnetworks have been widely reported in the research community of medical imaging we provide a focused literature survey on recent neural network developments in computer-aided diagnosis medical image ...
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Given that neuralnetworks have been widely reported in the research community of medical imaging we provide a focused literature survey on recent neural network developments in computer-aided diagnosis medical image segmentation and edge detection towards visual content analysts and medical image registration for Its pre-processing and post-processing with the aims of increasing awareness of how neuralnetworks can be applied to these areas and to provide a foundation for further research and practical development Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem (ii) how medical images could be analysed processed and characterised by neuralnetworks and (iii) how neuralnetworks could be expanded further to resolve problems relevant to medical imaging In the concluding section a highlight of comparison among many neural network applications is Included to provide a global view on computational intelligence with neuralnetworks in medical Imaging (C) 2010 Elsevier Ltd All rights reserved
A non-linear network structure called the fuzzy cellular neural network (FCNN) is presented. It is a reasonable extension of the cellular neural network (CNN) from classical to fuzzy sets. In this paper, structures of...
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A non-linear network structure called the fuzzy cellular neural network (FCNN) is presented. It is a reasonable extension of the cellular neural network (CNN) from classical to fuzzy sets. In this paper, structures of type ii FCNNs are presented. A type ii FCNN has fuzzy signals and crisp synaptic weights. Some theorems on the dynamical range and equilibrium points of type ii FCNNs are presented. applications of type ii FCNNs to min-max medical axis transformation, noise removal and edge detection under a low-SNR condition are presented. Computer simulation results are given. (C) 1997 by John Wiley & Sons, Ltd.
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 cod...
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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.
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