We present an efficient and fully parallel 2D object recognition method based on the use of a multiscale tree representation of the object boundary and recursive learning of trees. Specifically, the object is represen...
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We present an efficient and fully parallel 2D object recognition method based on the use of a multiscale tree representation of the object boundary and recursive learning of trees. Specifically, the object is represented by means of a tree where each node, corresponding to a boundary segment at some level of resolution, is characterized by a real vector containing curvature, length, and symmetry of the boundary segment, while the nodes are connected by arcs when segments at successive levels are spatially related. the recognition procedure is formulated as a training procedure made by recursive neural networks followed by a testing procedure over unknown tree structured patterns.
Artificial Neural Networks are applied for solving a wide variety of problems in several areas such as signal processing, robotics, diagnosis, and patternrecognition. these applications demand a high computing power ...
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Artificial Neural Networks are applied for solving a wide variety of problems in several areas such as signal processing, robotics, diagnosis, and patternrecognition. these applications demand a high computing power and the traditional software implementation are not sufficient. Hardware implementation of neural networks is very interesting due to its high performance and can easily be made parallel. this paper presents a hardware implementation of neural network after training and simulation on the MATLAB software. the excellent hardware performance has been performed through the use of field programmable gate array (FPGA). the diagnosis of the Multi-Purpose Research Reactor of Egypt accidents is used to test the proposed system.
Fractals are a promising framework for several applications other than image coding and transmission, such as database indexing, texture mapping and patternrecognition problems such as writer authentication. However,...
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Fractals are a promising framework for several applications other than image coding and transmission, such as database indexing, texture mapping and patternrecognition problems such as writer authentication. However, fractal based algorithms are strongly asymmetric because, in spite of the linearity of the decoding phase, the coding process is very time consuming. Many different solutions have been proposed for this problem, but there is not yet a standard for fractal coding. In this paper we analyze the problem of complexity reduction of the image coding phase and providing a new classification technique based on an approximation error measure. We show formally that postponing range/domain comparisons with respect to a preset block, it is possible to reduce the amount of operations needed to encode each range and therefore whole the image. the proposed strategy allows a drastic complexity reduction of the coding phase. the proposed method has been compared with another fractal coding method, showing in which circumstances the proposed algorithm performs better in terms of both bit rate and/or computing time.
the proceedings contain 95 papers. the topics discussed include: asymptotic predictions of the finite-sample risk of the k-nearest-neighbor classifier;a Bayesian framework for hierarchical relaxation;Solomon off codin...
ISBN:
(纸本)0818662751
the proceedings contain 95 papers. the topics discussed include: asymptotic predictions of the finite-sample risk of the k-nearest-neighbor classifier;a Bayesian framework for hierarchical relaxation;Solomon off coding as a means of introducing prior information in syntactic patternrecognition;reliable on-line human signature verification system for point-of-sales applications;learning algorithms for extended models of Boltzmann machines;useful information plane on pattern classification;acoustic spectral estimation using higher order statistics;projected subset least squares for robust linear prediction of speech;peak and power;a non-parametric minimax approach for robust speech recognition;classification of voiced and unvoiced speech by hierarchical stochastic modeling;an efficient vlsi architecture for template matching based on moment preserving pattern matching;and a hierarchical compression engine.
A generalized algorithm is introduced to mitigate difficulties encountered in practical implementations of the conventional projections-onto-constraint-sets algorithm. the theory and framework of this new procedure ar...
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Image processing and computer vision are natural applications for High Performance computing (here considered to be general-purpose parallel supercomputing), but there are many barriers to its effective use in compute...
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We have compared and improved several implementations of the Wiener filter to remove noise effects from Scanning Tunneling Microscope images. We have found that the implementation of Weisman et al. [6], using the nois...
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We present an algorithm for image segmentation with irregular pyramids. Instead of starting withthe original pixel grid, we first apply an adaptive Voronoi tessellation to the image. For irregular pyramid constructio...
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We present the overall goals of our research program on the application of high performance computing to remote sensing applications, specifically applications in land cover dynamics. this involves developing scalable...
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In this paper, we describe the design of an efficient VLSI architecture for image template matching. the hardware algorithm and architecture for template matching are based on a technique known as moment preserving pa...
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