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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing II"

共 2650 条记录，以下是2131-2140 订阅

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neural networks for local recognition of images with mixed noise

Neural networks for local recognition of images with mixed n...

引用

conference on applications of artificial neural networks in image processing VI

作者： Dolia, AN Lukin, VV Zelensky, AA Astola, JT Anagnostopoulos, C Natl Aerosp Univ Dept 504 UA-61070 Kharkov Ukraine

ISBN: (纸本)0819439835

A new approach to neural network (NN) application for local recognition of images with mixed noise is put forward. Although some pixels in images can be corrupted by spikes the proposed technique permits to eliminate uncertainty observed-in this case and to correctly recognize the pixels that, in fact, correspond to an edge, a homogeneous region or a small-sized object. For this purpose a procedure of recognition of one among four basic hypotheses and additional determination of spike properties within the scanning window is proposed. This recognition task is performed for two groups of outputs (classes) of one common NN. The problems of NN learning and structure selection for this case are discussed. The performance of neural network classifier is analyzed for different input data types and for various characteristics of noise. An improvement in correct recognition is shown for the proposed approach in comparison to previous work.

关键词： primary local recognition neural network image with mixed noise

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Achieving semantic coupling in the domain of high-dimensional video indexing application

Achieving semantic coupling in the domain of high-dimensiona...

引用

conference on applications of artificial neural networks in image processing VI

作者： Mittal, A Cheong, LF Natl Univ Singapore Dept Elect & Comp Engn Vis & Image Proc Lab Singapore 117576 Singapore

ISBN: (纸本)0819439835

A typical user of multimedia indexing system has to deal with low-level details like assignment of weights to different features and selection of threshold. To employ the system effectively and efficiently, one has to be versatile in similarity measures used, and other implementation issues. In order to overcome these shortcomings, researchers in the field of content-based retrieval are making efforts to develop semantic features. However, most of the work done in semantic features indexing build a priori model of a video's structure that is based on domain knowledge;and they are focused on narrow domains like extracting interesting shots from 'soccer', finding 'Tennis' shots etc. In this paper, an adequately domain-independent approach is presented where local features can characterize multimedia data using neural networks (ANN) and Support Vector Machines (SVM). In our previous work, we have shown that classification in content-based retrieval requires non-linear mapping of feature space. This can normally be accomplished by ANN and SVM. However, they inherently lack the capability to deal with meaningful feature evaluation and large dimensional feature space in the sense that they are inaccurate and slow. These defects can be overcome by employing meaningful feature selection on the basis of discriminative capacity of a feature. The experiments on database consisting of real video sequences show that the speed and accuracy of SVM can be improved substantially using this technique, while execution time can be substantially reduced for ANN. The comparison also shows that improved SVM turns out to be a better choice than ANN. Finally, it is shown that generalization in learning is not affected by reducing the dimension of the feature space by our method.

关键词： multimedia indexing dimensionality reduction neural networks support vector machines fast training relevant features evaluation optimum weight assignment elemental characterization

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An approach for 3D volumes matching

An approach for 3D volumes matching

引用

conference on applications of artificial neural networks in image processing VI

作者： Di Bona, S Marini, M Salvetti, O CNR Ist Elaboraz Informaz I-56100 Pisa Italy

ISBN: (纸本)0819439835

In 3D Computer Vision a relevant problem is to match a "Source" image dataset with a "Target" image dataset, that is to find the rule that controls the modification of the global characteristics of the Source in such a way as to match the Target. The matching problem can be faced using a neural net approach;where the nodes are related to the image voxels and the synapses to the voxel information, e.g. locations;grey values, gradients, angles. This paper presents the "volume-matcher 3D" project, an approach for a data-driven comparison and registration of three-dimensional images. The approach proposes a neural network model derived from the 'Self Organizing Maps' and extended in order to match a full 3D data set of a 'source volume: with the 3D data set of a 'target volume'. The algorithms developed have been tested on real cases of interest in medical imaging. The results have been evaluated on the basis of both the Mean Square Error and the visual analysis, performed by an expert, of the result volume. The software has been implemented on a high performance PC using AVS/Express (TM) software package for volume reconstruction;'polytri' based algorithms have been used for this purpose.

关键词： three-dimensional neural networks 3D deformation modelling volume approximation surface mesh brain MRI

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A versatile learning by sample strategy to perform statistical pattern classification in biomedical images

A versatile learning by sample strategy to perform statistic...

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conference on applications of artificial neural networks in image processing VI

作者： Fertil, B Brahmi, D Cassoux, N Serruys, C Giron, A CHU Pitie Salpetriere INSERM U494 F-75634 Paris 13 France

ISBN: (纸本)0819439835

Medical images represent meaningful visual stimuli for experienced physicians but their characteristics are frequently hard to decipher. The development of a computational model to capture physicians' expertise is a demanding task, especially if a significant and sophisticated preprocessing of images is to be required. Learning from validated images may be a more convenient approach, inasmuch a large and representative bunch of samples is available. Our approach mimics physicians' behavior: images are parsed, looking for the presence of specific features. The analysis process involves several steps: image preprocessing, feature extraction and classification, labeling of areas on the basis of recognized features. Modules specialized in the detection of specific features are built using a learning by sample method, which takes advantage of large data bases of typical images, carefully labeled by several clinicians. Learning essentially relies on the neuronal network paradigm. In this paper, the architecture of an all-round pattern recognition system is described together with some of the results obtained when dealing with the characterization of infected areas in retinal angiograms. Several tricky aspects of the implementation are discussed. In particular, special attention is given to the feature-extraction scheme and the learning data base pertinency.

关键词： statistical pattern recognition supervised and unsupervised learning evaluation image segmentation retinal angiogram

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Novel VLSI architecture for edge detection and image enhancement on video camera chips

Novel VLSI architecture for edge detection and image enhance...

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conference on Sensors and Camera Systems for Scientific, Industrial, and Digital Photography applications

作者： Hammadou, T Bouzerdoum, A Bermak, A Boussaid, F Biglari, M Motorola Australian Res Ctr Botany NSW 1455 Australia

ISBN: (纸本)0819439843

In this paper an image enhancing technique is described. It is based on Shunting Inhibitory Cellular neural networks (SICNN). As the limitation of the linear approaches to image coding, enhancement, and feature extraction became apparent, research in image processing began to disperse into the three goal-driven directions. However Shunting Inhibitory Cellular neural networks model simultaneously addresses the three problems of coding, enhancement, and extraction, as it acts to compress the dynamic range, reorganize the signal to improve visibility, suppress noise, and identify local features. The algorithm we are describing is simple and cost-effective, and can be easily applied in real-time processing for digital still camera application.

关键词： shunting inhibitory cellular neural networks (SICNN) digital still camera CMOS imagers

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Automatic generation of multipath algorithms in the cellular nonlinear network

Automatic generation of multipath algorithms in the cellular...

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conference on applications of artificial neural networks in image processing VI

作者： Preciado, VM Guinea, D Montúfar, R Spanish Council Sci Res Inst Automat Ind Madrid 28500 Spain

ISBN: (纸本)0819439835

The traditional image processing techniques require a lot of computational effort due to data on each pixel are computed in a sequential way and the path of information is an A/D converter. The delay accumulation create in this process is unacceptable in real time image processing because of the high information flow managed in the usual vision tasks (e.g. automatic industrial inspection, vision problems in robotics, pattern analysis, etc.). Thus, the use of a massive parallel architecture working with analog signals avoids the previous problems. This is just the basis idea of Cellular neural Network (CNN's): an array of analogic dynamic processors which cells interact directly within a finite local neighborhood. The local CNN connectivity allow its realization as VLSI chips that can operate at a very high speed and complexity. Nowadays CNN architectures implemented as VLSI chips shows the aptitude of extremely high speed compared with traditional digital image processing tools. The proliferation of more and more sophisticated CNN architectures, and the increasing effort to implant practical system based in CNN chips, make important the development of analog algorithm to perform complex image processing tasks dedicated to many different fields, i.e. industrial applications, robotic systems and pattern recognition. The objective of this work is to generate a learning machine capable of find solutions for complex image processing task by CNN's. First a general machine for automatic analog algorithm design independent of the problem to solve is created, this is accomplished through an evolutionary strategy that is an extension of genetic programming. Second, this work introduces a suite of sub-mechanisms that increase the power of genetic programming and contribute to reduce the enormous space search for producing a plentiful search. Some concepts in this section are related with AI theory, in such a way that in this work we are in the intersection field of AI and Imag

关键词： cellular neural network visual processing parallel computation peal time processing automatic templates generation genetic algorithms

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Classification of segmented images combining neural networks and wavelet matching

Classification of segmented images combining neural networks...

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conference on applications of artificial neural networks in image processing VI

作者： Flores-Pulido, L López-López, A Altamirano-Robles, L Natl Inst Astrophys Opt & Elect Postgrad Program Computat Sci Puebla 72840 Mexico

ISBN: (纸本)0819439835

The need for content-based image retrieval is staggering given the information explosion. The advantage of systems for content-based retrieval is their effectiveness to find relevant images when searching in large collections. We believe that an improved technique for retrieval can be achieved by combining techniques. So, we approach the problem in two stages: an organization and retrieval phase, employing different techniques in each. First phase classifies the images based on a preliminary feature extraction process. This feature extraction gathers a set of statistical parameters. After the extraction, the images are classified employing an ART2 neural network model of 12 inputs and 16 outputs. Adaptive resonance architectures are networks that self-organize stable pattern recognition codes in real-time in response to arbitrary sequences of input patterns. The retrieval phase allows searching and relies on a wavelet matching process. This process works on representative images, and has been successful in information retrieval tasks. The wavelet Haar transform employed can be estimated quickly and tends to produce blocks in the image details. The wavelet process is applied in L-2. A similarity assessment process with an associated measure is necessary for wavelet matching. We report in detail the phases, and the preliminary results already achieved.

关键词： content-based image retrieval wavelet matching similarity measure neural networks ART2 regions Haar wavelet transform wavelet coefficients retrieval system

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A new technique for real-time distortion-invariant multiobject recognition and classification

A new technique for real-time distortion-invariant multiobje...

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conference on applications of artificial neural networks in image processing VI

作者： Hong, RT Li, XS Hong, E Wang, ZY Wei, HG SW Inst Tech Phys Chengdu 610041 Sichuan Peoples R China

ISBN: (纸本)0819439835

A real-time hybrid distortion-invariant OPR system was established to make 3-D multiobject distortion-invariant automatic pattern recognition. Wavelet transform technique was used to make digital preprocessing of the input scene, to depress the noisy background and enhance the recognized object. A three-layer backpropagation artificial neural network (ANN) was used in correlation signal post-processing to perform multiobject distort ion-invariant recognition and classification. The C-80 and NOA real-time processing ability and the multithread programming technology were used to perform high speed parallel multitask processing and speed up the post processing rate to ROIs. The reference filter library (RFL) was constructed for the distortion version of 3-D object model images based on the distortion parameter tolerance measuring as rotation, azimuth and scale. The real-time optical correlation recognition testing of this OPR system demonstrates that using the preprocessing, post-processing, the nonlinear algorithm of optimum filtering, RFL construction technique and the multithread programming technology, a high possibility of recognition and recognition rate were obtained for the real-time multiobject distortion-invariant OPR system. The recognition reliability and rate was improved greatly. These techniques are very useful to automatic target recognition.

关键词： real-time distortion-invariant multiobject recognition back-propagation artificial neural Network(ANN) ROI(Region of Interest) NOA(Neighborhood Operations Accelerator) OPR(Optical Pattern Recognition)

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Proceedings of the Joint 9th IFSA World Congress and 20th NAFIPS International conference

Proceedings of the Joint 9th IFSA World Congress and 20th NA...

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Joint 9th IFSA World Congress and 20th NAFIPS International conference

This Volume 2 of the conference proceedings contains 110 papers. Topics discussed include fuzzy artificial intelligence, soft computing applications to intelligent manufacturing and fault diagnosis, intelligent communication, fuzzy relations, pattern recognition, evolutionary algorithms, fuzzy modeling, math, soft computing in medicine, control, finance markets, fuzzy set techniques in subjective evaluation and decision, neural networks and fuzzy techniques, neural networks and pattern recognition, fuzzy systems for intelligent control, information mining, soft computing in image processing, fuzzy topology, genetic fuzzy systems and data mining and clustering.

关键词： Fuzzy sets

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Fast principal component extraction by a homogeneous neural network

Fast principal component extraction by a homogeneous neural ...

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IEEE International conference on Acoustics, Speech, and Signal processing

作者： Ouyang, S Bao, Z Guilin Univ Elect Technol Dept Commun & Informat Guilin 541004 Guangxi Peoples R China

ISBN: (纸本)0780370414

On the basis of the concepts of both weighted subspace criterion and information maximization, this paper proposes a weighted information criterion (WINC) for searching for the optimal solution of a homogeneous neural network. We develop two adaptive algorithms based on the WINC for extracting in parallel multiple principal components. The both algorithms are be able to provide an adaptive step size which leads to a significant improvement in the learning performance. Furthermore, the recursive least squares (RLS) version of WINC algorithms has a low computational complexity O(Np), where N is the input vector dimension and p is the number of desired principal components. Since the weighting matrix does not require an accurate value, it facilitates the system design of the WINC algorithm for real applications. Simulation results are provided to illustrate the effectiveness of WINC algorithms for PCA.

关键词： neural networks

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