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A lossless image compression algorithm using variable block size segmentation

A lossless image compression algorithm using variable block ...

引用

International conference on Pattern Recognition

作者： N. Ranganathan S.G. Romaniuk K.R. Namuduri Center for Microelectronics Research University of South Florida Tampa FL USA Department of Information Systems National University of Singapore Singapore GTE Information Systems Tampa FL USA

In this paper, we present an analysis of two image characteristics which give rise to local and global redundancy in image representation. Based on this study, we propose a lossless image compression scheme which exploits both types of redundancy. the algorithm segments the image into variable size blocks and encodes them depending on characteristics exhibited by the pixels within the block. the performance of the proposed algorithm is studied by software implementation. the proposed algorithm works better than other lossless compression schemes such as the Huffman, the arithmetic, the Lempel-Ziv and the JPEG.

关键词： Image coding Image segmentation Image analysis Information systems Lighting Shadow mapping Performance analysis Microelectronics Information analysis Image representation

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Knowledge based (re-)clustering

Knowledge based (re-)clustering

引用

International conference on Pattern Recognition

作者： M.C. Clark L.O. Hall Chunlin Li D.B. Goldgof Department of Computer Science and Engineering University of South Florida Tampa FL USA

this paper presents a novel multiparadigm segmentation method based upon knowledge based clustering with reclustering. the techniques described enhance unsupervised classification and achieve pattern labeling. First domain knowledge is utilized to decide where and how a clustering algorithm is applied, then clustering is iteratively applied to focus-of-attention patterns with the knowledge of how many expected classes there are in those patterns and the prototypical patterns of a class. Examples showing clustering improvements are given from brain MRI's and satellite images.

关键词： Clustering algorithms Knowledge engineering Focusing Prototypes Satellites Training data Expert systems computer science Labeling Iterative algorithms

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A branch-and-bound algorithm for optimization of multiperspective classifier

A branch-and-bound algorithm for optimization of multiperspe...

引用

International conference on Pattern Recognition

作者： M.W. Kurzynski E. Puchala A. Blinowska Institute of Control & Systems Engineering Technical University of Wroclaw Wroclaw Poland Service d' Informatique Medicale Hospital Broussais Paris France

the present paper is devoted to the multiperspective recognition, in which the pattern to be recognized undergoes several classification tasks. Each task denotes here recognition from a different point of view and with respect to a different set of classes. In the decomposed dependent approach, when the multiperspective recognition is not a single activity but it states the multistep decision process, important role plays the order of recognition tasks determining the successive steps of the entire multiperspective recognition. In this paper the algorithm for optimal (with respect to the risk function) ordering of recognition tasks is presented. the proposed algorithm using controlled enumeration through a branch-and-bound search procedure selects the best order without exhaustive search. Furthermore, results of computer experiments are given and a simple illustrative example is considered.

关键词： Pattern recognition Diseases Medical control systems Control systems Biomedical engineering systems engineering and theory Medical treatment Search methods Random variables Probability density function

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A robust variable length nonlinear filter for edge enhancement and noise smoothing

A robust variable length nonlinear filter for edge enhanceme...

引用

International conference on Pattern Recognition

作者： R.L. Kashyap Department of Electrical Engineering Purdue University West Lafayette IN USA

Most of the recent work in nonlinear order statistic filters has focused on smoothing and preserving the details in digital images. However, in many image analysis and computer vision applications, where edges are being used as primary features, edge enhancement becomes an essential attribute of preprocessing filters. Moreover, the human visual system is also very sensitive to this feature. Majority of the most frequently used filters, such as median and its extensions do not posses this property; median filters tend to preserve any monotonic degradation of the edge and therefore, are not capable of enhancing blurred or ramp edges. In this paper we present a variable length robust nonlinear filter which has the capability to both sharpen the edges and smoothing out the noise. Experimental results on real images are also provided.

关键词： Robustness Nonlinear filters Smoothing methods Statistics Digital filters Digital images Image edge detection computer vision Application software Humans

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Empirical risk minimization versus maximum-likelihood estimation: A case study

Empirical risk minimization versus maximum-likelihood estima...

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International conference on Pattern Recognition

作者： R. Meir Department of Electrical Engineering Haifa Israel

Considers a simple two class pattern classification problem from two points of view, namely that of empirical risk minimization and that of maximum-likelihood estimation. the main focus is on an exact solution for the generalization error resulting from the above two approaches, emphasizing mainly the finite sample behavior, which is very different for the two methods. Focusing on the case of normal input distributions and linear threshold classifiers, the author uses statistical mechanics techniques to calculate the empirical and expected (or generalization) errors for the maximum-likelihood and minimal empirical error estimation methods, as well as several other algorithms. In the case of spherically symmetric distributions within each class the author finds that the simple Hebb rule, corresponding to maximum-likelihood parameter estimation, outperforms the other more complex algorithms, based on error minimization. Moreover, the author shows that in the regime where the overlap between the classes is large, algorithms with low empirical error do worse in terms of generalization, a phenomenon known as over-training.

关键词： Risk management Maximum likelihood estimation computer aided software engineering Pattern recognition Error analysis Parameter estimation Probability distribution H infinity control Heart Convergence

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A single chip multi-function sensor system for wood inspection

A single chip multi-function sensor system for wood inspecti...

引用

International conference on Pattern Recognition

作者： E. Astrand A. Astrom Department of Electrical Engineering Linkoping University Linkoping Sweden

Image processing is used in the forest products industry to detect various defects on wood surfaces. Normally, several different sensors are needed, which makes the systems complicated and expensive. In this paper we present a highly integrated sensor system for wood defect detection based on a single MAPP2200 smart sensor. Five different measuring principles are simultaneously utilized to detect surface grayscale, surface roughness, deviating grain direction, 3D-profile and surface density. Using a pixel resolution of 1/spl times/0.5 mm, the sensor permits scanning of boards at the speed of several meters per second.

关键词： Sensor systems Inspection Intelligent sensors Rough surfaces Surface roughness Gray-scale Image sensors Logic arrays Photodiodes Image processing

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Asymptotic predictions of the finite-sample risk of the k-nearest-neighbor classifier

Asymptotic predictions of the finite-sample risk of the k-ne...

引用

International conference on Pattern Recognition

作者： R.R. Snapp S.S. Venkatesh Department of Computer Science and Electrical Engineering University of Vermont Burlington VT USA Department of Electrical Engineering University of Pennsylvania Philadelphia PA USA

the finite-sample risk of the k-nearest-neighbor classifier is analyzed for a family of two-class problems in which patterns are randomly generated from smooth probability distributions in an n-dimensional Euclidean feature space. First, an exact integral expression for the m-sample risk is obtained for a k-nearest-neighbor classifier that uses a reference sample of m labeled feature vectors. Using a multidimensional application of Laplace's method of integration, this integral can be represented as an asymptotic expansion in negative rational powers of m. the leading terms of this asymptotic expansion elucidate the curse of dimensionality and other properties of the finite-sample risk.

关键词： Risk analysis Pattern analysis computer science Probability distribution Multidimensional systems Pattern recognition Extraterrestrial measurements Performance analysis Convergence Yield estimation

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A communication module for parallel image analysis on the transputer image processing system

A communication module for parallel image analysis on the tr...

引用

International conference on Pattern Recognition

作者： U. Buker B. Mertsching Department of Electrical Engineering University of Paderborn Paderborn Germany

this paper presents a flexible communication module for low-level as well as high-level image processing operations. It allows a good separation of data communication and data processing and thereby reduces the necessary amount of work for the implementation of parallel image processing algorithms. It supports heterogenous processor systems. It has been successfully used for the parallel implementation of a hierarchical image transition and for its symbolic analysis on a 9-node transputer image processing system. Experimental results in the field of traffic sign detection are discussed.

关键词： Image analysis Image processing Data communication Data processing Displays Noise reduction Noise level Image segmentation Parallel architectures Object recognition

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High performance computing for land cover dynamics

High performance computing for land cover dynamics

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International conference on Pattern Recognition

作者： R. Parulekar L. Davis R. Chellappa J. Saltz A. Sussman J. Townshend UMIACS College Park MD USA Institute for Advanced Computer Studies (UMIACS) University of Maryland College Park MD USA Department of Electrical Engineering University of Padova Italy Department of Geography University of Reading UK Department of Computer Science Tsinghua University Taiwan Department of Computer Science University of California USA

Presents the overall goals of the authors' research program on the application of high performance computing to remote sensing applications, specifically applications in land cover dynamics. this involves developing scalable and portable programs for a variety of image and map data processing applications, eventually integrated with new models for parallel I/O of large scale images and maps. After an overview of the multiblock PARTI run time support system, the authors explain extensions made to that system to support image processing applications, and then present an example involving multiresolution image processing. Results of running the parallel code on both a TMC CM5 and an Intel Paragon are discussed.

关键词： High performance computing Object oriented modeling Image processing Application software Spatial resolution Data structures Remote sensing Image resolution Image analysis Dynamic programming

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Geometric invariance in space-variant vision systems: the exponential chirp transform

Geometric invariance in space-variant vision systems: the ex...

引用

International conference on Pattern Recognition

作者： G. Bonmassar E.L. Schwartz Department Biomedical Engineering Boston University Boston MA USA Department Cognitive and Neural Systems Boston University Boston MA USA

Outlines a method to derive geometric invariance kernels which may be applied to a space-variant sensor architecture. the basic idea as to transform a kernel with desired symmetry properties (e.g. the Fourier kernel) in the domain to the range of the transform. By combining this transformed kernel with the Jacobian of the transformation, the authors obtain a new integral transform, in the range, which has similar properties to the original transform. the authors illustrate this idea with a variant of the Mellin-Fourier transform, applied to an image which has been transformed by a log-polar mapping. the kernel obtained, which the authors call an "exponential chirp" has properties (unlike the Mellin-Fourier transform) which are both consistent with the spatial nature of human vision and can be applied directly in the space-variant image plane. the authors outline applications to visual template matching and auto-correlation, and show a one-dimensional example of a generalization of cepstral auto-correlation using this method.

关键词： Machine vision Chirp Kernel Fourier transforms Jacobian matrices Autocorrelation computer architecture Optical computing Biomedical engineering Biosensors

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