Modern video encoding techniques generate variable bit rates, because they take advantage of different rates of motion in scenes, in addition to using lossy compression within individual frames. We have introduced a n...
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ISBN:
(纸本)0819439835
Modern video encoding techniques generate variable bit rates, because they take advantage of different rates of motion in scenes, in addition to using lossy compression within individual frames. We have introduced a novel method for video compression based on temporal subsampling of video frames, and for video frame reconstruction using neural network based function approximations. In this paper we describe another method using wavelets for still image compression of frames, and function approximations for the reconstruction of subsampled frames. We evaluated the performance of the method in terms of observed traffic characteristics for the resulting compressed and subsampled frames, and in terms of quality versus compression ratio curves with real video image sequences. Comparisons are presented with other standard methods.
artificialneural network (ANN) plays an important role in many medical imaging applications. The detection of cervical cancer cells uses an ANN for classifying the normal and abnormal cells in the cervix region of th...
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artificialneural network (ANN) plays an important role in many medical imaging applications. The detection of cervical cancer cells uses an ANN for classifying the normal and abnormal cells in the cervix region of the uterus. Cervical cancer detection is very challenging because this cancer occurs without any symptoms. The classification between the normal, abnormal and cancerous cells is identified by using an artificialneural network which produces accurate results than the manual screening methods like Pap smear and Liquid cytology based (LCB) test. The ANN uses several architectures for easy and accurate detection of cervical cells. In this paper, a survey and analysis on the different types of architecture in the ANN with its accuracy results and performance are discussed. A brief description about the working and detection of cervical cancer is presented which is useful for the classification of normal and abnormal cervical cells. (C) 2016 The Authors. Published by Elsevier B.V.
With the success of deep learning in computer vision applications, deep learning based algorithms have also been proposed for imageprocessingapplications. One such application is lossless image compression. Most tra...
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ISBN:
(纸本)9781665450928
With the success of deep learning in computer vision applications, deep learning based algorithms have also been proposed for imageprocessingapplications. One such application is lossless image compression. Most traditional lossless image compression algorithms use pixel-by-pixel processing algorithms. In the pixel-by-pixel algorithms, each pixel is predicted from the previously coded neighbor pixels and the prediction error is compressed without loss. Deep learning based lossless image compression algorithms can be categorized into two categories, namely prior based algorithms and pixel-by-pixel (or masked convolution based) algorithms. In the pixel-by-pixel algorithms, each pixel's probability distribution is obtained by processing the previously coded neighboring pixels with a neural network, which is then used by an arithmetic coder for lossless compression. This paper explores a deep learning based architecture, which utilizes masked convolutions, to model probability distributions of pixels and also presents a method to improve the parallelization of the algorithms. The obtained compression performance is competitive and is compared to both state-of-the art traditional and deep learning based methods.
The proceedings contains 79 papers. Following topics are discussed;theory;time series;control;imageprocessing;novel applications;pattern recognition;pulse-coupled neuralnetworks;physics applications;physical theory ...
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ISBN:
(纸本)0819421413
The proceedings contains 79 papers. Following topics are discussed;theory;time series;control;imageprocessing;novel applications;pattern recognition;pulse-coupled neuralnetworks;physics applications;physical theory of artificialneuralnetworks;hardware-oriented implementations;and software-oriented implementations.
This paper depicts the restructuring of different models of third generation of artificialneural network, that is, the spiking neuralnetworks for imageprocessingapplications. The proposed work aims towards impleme...
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ISBN:
(纸本)9781479925605
This paper depicts the restructuring of different models of third generation of artificialneural network, that is, the spiking neuralnetworks for imageprocessingapplications. The proposed work aims towards implementation of a novel algorithm using different models of Spiking neuralnetworks which will improve upon the optimization results in the field of imageprocessing. In this paper, we focus on various evaluation parameters like mean square error, mean absolute error peak signal to noise ratios as well as enhance the output using ANN as wellas Leaky Integrate and firing Model of Spiking neuralnetworks.
In this paper, a Multi Layer Perceptron (MLP) based artificial Immune System (AIS) is presented for breast cancer classification. The proposed algorithm integrates clonal selection principle of AIS in MLP learning to ...
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ISBN:
(纸本)9781479986378
In this paper, a Multi Layer Perceptron (MLP) based artificial Immune System (AIS) is presented for breast cancer classification. The proposed algorithm integrates clonal selection principle of AIS in MLP learning to reduce its computational costs and accelerate its convergence to a Mean Squared Error Threshold (MSEth) set by the user. Applied on the Wisconsin Diagnosis Breast Cancer database (WDBC), the results show that combining artificial Immune Systems and neuralnetworks is effective. Indeed, a significant reduction of computation time has been obtained with a slight improvement of classification accuracy.
The proceedings contains 14 papers from the conference on SPIE: applications of artificialneuralnetworks in imageprocessing ViiI. The topics discussed include: selective visual attention in object detection process...
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The proceedings contains 14 papers from the conference on SPIE: applications of artificialneuralnetworks in imageprocessing ViiI. The topics discussed include: selective visual attention in object detection processes;application of genetic algorithms and neuralnetworks in the automatic classification of leukocytes;character recognition by synergetic neural network based on selective attention parameters and dynamic electrical impedance tomography method based on a multilevel BP neural network.
We present a new technique for extracting the direction map from fingerprints. The fingerprint image is first partitioned into small image blocks. Then, a set of parameters is extracted from each block and fed into a ...
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ISBN:
(纸本)0819418455
We present a new technique for extracting the direction map from fingerprints. The fingerprint image is first partitioned into small image blocks. Then, a set of parameters is extracted from each block and fed into a neural network that outputs the preferential direction for each block. The technique performed very well in operational conditions. It was developed to be employed in an Automatic Fingerprint Classification System.
In this article we present a speech recognition system for isolated words based on neuralnetworks and explore its behavior. We applied Feedforward artificialneuralnetworks of different complexities and various sequ...
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ISBN:
(纸本)9781538669792
In this article we present a speech recognition system for isolated words based on neuralnetworks and explore its behavior. We applied Feedforward artificialneuralnetworks of different complexities and various sequence length normalization techniques. The system uses well-known MFCC speech features widely deployed in variety of speech applications, together with cepstral mean normalizations to help the classifier to reach higher accuracy and robustness. Apart of that we suggested eligible scenarios to trim unequal speech sequences to fixed length training inputs for Feedforward networks. Finally, an extra parameter of the length of original utterances was explicitly added to a feature vector to test its importance for the recognition process using neuralnetworks. All experiments and evaluations were accomplished on the professional database used to train recognition systems and compared to a standard approach based on continuous density Hidden Markov Models.
Both genetic algorithms (GAs) and artificialneuralnetworks (ANNs) (connectionist learning models) sue effective generalisations of successful biological techniques to the artificial realm. Both techniques are inhere...
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ISBN:
(纸本)0897918622
Both genetic algorithms (GAs) and artificialneuralnetworks (ANNs) (connectionist learning models) sue effective generalisations of successful biological techniques to the artificial realm. Both techniques are inherently parallel and seem ideal for implementation on the current generation of parallel supercomputers. We consider how he two techniques complement each other and how combining them (i.e. evolving artificialneuralnetworks with a genetic algorithm), may give insights into the evolution of structure and modularity in biological brains. The incorporation of evolutionary and modularity concepts into artificial systems has the potential to decrease the development time of ANNs for specific image and information processingapplications, General considerations when genetically encoding ANNs are discussed, and a new encoding method developed, which has the potential to simplify the generation of complex modular networks. The implementation of this technique on a CM-5 parallel supercomputer raises many practical and theoretical questions in the application and use of evolutionary models with artificialneuralnetworks.
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