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Conference on mathematics of data/image pattern Coding, compression, and encryption with applications XIII

作者： Schmalz, Mark S. Ritter, Gerhard X. Hayden, Eric Key, Gary Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL 32611 USA Frontier Technol Inc Altamonte Springs FL 32701 USA

ISBN: (纸本)9780819487469

In Bayesian pattern recognition research, static classifiers have featured prominently in the literature. A static classifier is essentially based on a static model of input statistics, thereby assuming input ergodicity that is not realistic in practice. Classical Bayesian approaches attempt to circumvent the limitations of static classifiers, which can include brittleness and narrow coverage, by training extensively on a data set that is assumed to cover more than the subtense of expected input. Such assumptions are not realistic for more complex pattern classification tasks, for example, object detection using pattern classification applied to the output of computer vision filters. In contrast, we have developed a two step process, that can render the majority of static classifiers adaptive, such that the tracking of input nonergodicities is supported. Firstly, we developed operations that dynamically insert (or resp. delete) training patterns into (resp. from) the classifier's pattern database, without requiring that the classifier's internal representation of its training database be completely recomputed. Secondly, we developed and applied a pattern replacement algorithm that uses the aforementioned pattern insertion/deletion operations. This algorithm is designed to optimize the pattern database for a given set of performance measures, thereby supporting closed-loop, performance-directed optimization. This paper presents theory and algorithmic approaches for the efficient computation of adaptive linear and nonlinear pattern recognition operators that use our pattern insertion/deletion technology - in particular, tabular nearest-neighbor encoding (TNE) and lattice associative memories (LAMs). Of particular interest is the classification of nonergodic datastreams that have noise corruption with time-varying statistics. The TNE and LAM based classifiers discussed herein have been successfully applied to the computation of object classification in hyperspectral re

关键词： pattern recognition Adaptive pattern classification Neural networks

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Massively Parallel Computation of Lattice Associative Memory Classifiers on Multicore Processors

Massively Parallel Computation of Lattice Associative Memory...

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Conference on mathematics of data/image pattern Coding, compression, and encryption with applications XIII

作者： Ritter, Gerhard X. Schmalz, Mark S. Hayden, Eric T. Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL 32611 USA

ISBN: (纸本)9780819487469

Over the past quarter century, concepts and theory derived from neural networks (NNs) have featured prominently in the literature of pattern recognition. Implementationally, classical NNs based on the linear inner product can present performance challenges due to the use of multiplication operations. In contrast, NNs having nonlinear kernels based on Lattice Associative Memories (LAM) theory tend to concentrate primarily on addition and maximum/minimum operations. More generally, the emergence of LAM-based NNs, with their superior information storage capacity, fast convergence and training due to relatively lower computational cost, as well as noise-tolerant classification has extended the capabilities of neural networks far beyond the limited applications potential of classical NNs. This paper explores theory and algorithmic approaches for the efficient computation of LAM-based neural networks, in particular lattice neural nets and dendritic lattice associative memories. Of particular interest are massively parallel architectures such as multicore CPUs and graphics processing units (GPUs). Originally developed for video gaming applications, GPUs hold the promise of high computational throughput without compromising numerical accuracy. Unfortunately, currently-available GPU architectures tend to have idiosyncratic memory hierarchies that can produce unacceptably high data movement latencies for relatively simple operations, unless careful design of theory and algorithms is employed. Advantageously, some GPUs (e. g., the Nvidia Fermi GPU) are optimized for efficient streaming computation (e. g., concurrent multiply and add operations). As a result, the linear or nonlinear inner product structures of NNs are inherently suited to multicore GPU computational capabilities. In this paper, the authors' recent research in lattice associative memories and their implementation on multicores is overviewed, with results that show utility for a wide variety of pattern classifica

关键词： pattern recognition Adaptive pattern classification Neural networks

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mathematics of data/image pattern Coding, compression, and encryption with applications XIII

Mathematics of Data/Image Pattern Coding, Compression, and E...

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mathematics of data/image pattern Coding, compression, and encryption with applications XIII

ISBN: (纸本)9780819487469

The proceedings contain 18 papers. The topics discussed include: noise tolerant dendritic lattice associative memories;algorithms for adaptive nonlinear pattern recognition;massively parallel computation of lattice associative memory classifiers on multicore processors;fractal-based watermarking of color images;the optimum discrete scan-type approximation of low-pass type signals bounded by a measure like Kullback-Leibler divergence;extension of the concept of multi-legged-type signals to the optimum multidimensional running approximation of multidimensional band-limited signals;limited-photon 3D image recognition using photon-counting integral imaging;aligning images with cad models via quaternion optimization;the optimum approximation of multidimensional vector signals by multi-input multi-output matrix filter banks;multidimensional feature extraction from 3D hyperspectral images;and a comparative test of different compression methods applied to solar images.

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Edge pattern Analysis for Edge Detection and Localization

Edge Pattern Analysis for Edge Detection and Localization

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Conference on mathematics of data/image pattern Coding, compression, and encryption with applications XIII

作者： Jiang, Bo NIA Hampton VA 23666 USA

ISBN: (纸本)9780819487469

Edge detection process plays an important role in image processing, and at its most basic level classifies image pixels into edges and non-edge pixels. The accuracy of edge detection methods in general image processing determines the eventual success or failure of computerized analysis procedures which follow the initial edge detection determinations. In view of this downstream impact on pattern processing, considerable care should be taken to improve the accuracy of the front-end edge detection. In general, edges would be considered as abrupt changes or discontinuity in intensity of an image. Therefore, most of edge detection algorithms are designed to capture signal discontinuities but the spatial character of especially complex edge patterns has not received enough attention. Edges can be divided into basic patterns such as ramp, impulse, and step: different types have different shapes and consequent mathematical properties. In this paper, the behavior of various edge patterns, under different order derivatives in the discrete domain, are examined and analyzed to determine how to accurately detect and localize these edge patterns, especially reducing double edge response that is one important drawback to the derivative method. General rules about the depiction of edge patterns are proposed. Asides from the ideal patterns already described, other pattern types, such as stair and roof, are examined to broaden the initial analysis. Experiments conducted to test my propositions support the idea that edge patterns are instructive in enhancing the accuracy of edge detection and localization.

关键词： Edge pattern Edge pattern Analysis Ramp edge Step edge Impulse Edge Edge Detection Localization

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Proceedings of SPIE - mathematics of data/image pattern recognition, compression, and encryption with applications ix

Proceedings of SPIE - Mathematics of Data/Image Pattern Reco...

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mathematics of data/image pattern recognition, compression, and encryption with applications ix

ISBN: (纸本)0819463949

The proceedings contain 23 papers. The topics discussed include: biorthogonal wavelets of maximum coding through pseudoframes for subspaces;a skinning prediction scheme for dynamic 3D mesh compression;multi-modal multi-fractal boundary encoding in object-based image compression;perspectives on data compression methods for network-level management of multi-sensor systems;exploiting data compression methods for network-level management of multi-sensor systems;a model utilizing artificial neural network for perceptual image;use of adaptive models in watermark identification;data payload optimality: a key issue for for video watermarking applications;quantization index modulation-based watermarking using holography;the optimum estimation of statistical signals based on systematic expression of many types of sample arrays in multidimensional space;and segmentation of motion textures using mixed-state Markov random fields.

关键词： pattern recognition

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image registration error analysis using pattern recognition algorithms

Image registration error analysis using pattern recognition ...

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Conference on mathematics of data/image Coding, compression, and encryption with applications XII

作者： Duraisamy, Prakash Shen, Yao Yuan, Xiaohui Univ N Texas Denton TX 76203 USA

ISBN: (纸本)9780819482952

image segmentation is one of the important applications in computer vision applications. In this paper, we present an image registration method that stiches multiple images into one complete view. Also, we demonstrate how image segmentation is used as an error metric to evaluate image registration. This paper explains about the error analysis using pattern recognition algorithm such as watershed algorithm for calculating the error for image registration applications. In this paper, we compare pixel intensity-based error metric with object-based error metric for evaluating the registration results. We explain in which situation pattern recognition algorithm is superior to other conventional algorithm such as mean square error.

关键词： Homography image Registration Evaluation

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Introduction to part A: Conference on mathematics of image/data pattern coding, compression, and encryption with applications XII

Proceedings of SPIE - The International Society for Optical ...

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Proceedings of SPIE - The International Society for Optical Engineering 2009年 7444卷 ix-xi页

作者： Schmalz, Mark S. Ritter, Gerhard X. CBarrera, Junior Astola, Jaakko T.

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Proceedings of SPIE: mathematics of data/image pattern recognition, compression, and encryption with applications XI

Proceedings of SPIE: Mathematics of Data/Image Pattern Recog...

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mathematics of data/image pattern recognition, compression, and encryption with applications XI

ISBN: (纸本)9780819472953

The proceedings contain 22 papers. The topics discussed include: data compression in emitter location systems via sensor pairing and selection;impact of wavelet types on image data characteristics during compression;optimization of HVS-based objective image quality assessment with eye tracking;lossless compression of images with permutation codes;neural network-based watermark embedding and identification;an image watermark tutorial tool using Matlab;the optimum approximation of an orthogonal expansion having bounded higher order correlations of stochastic coefficients;simultaneous minimization of various worst case measures of error in FIR filter bank;flash hyperspectral imaging of non-stellar astronomical objects;area-based novel approach for fuzzy edge detection using type II fuzzy sets;and compensation method for quantitative observation of multicolor fluorescence with nonlinear mapping.

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mathematics of data/image pattern recognition, compression, and encryption with applications XI: Introduction

Proceedings of SPIE - The International Society for Optical ...

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Proceedings of SPIE - The International Society for Optical Engineering 2008年 7075卷 ix-xi页

作者： Schmalz, Mark S. Ritter, Gerhard X. Barrera, Junior Astola, Jaakko T.

No abstract available

ISBN: (纸本)9780819472953

No abstract available

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Proceedings of SPIE - mathematics of data/image pattern recognition, compression, Coding, and encryption X, with applications

Proceedings of SPIE - Mathematics of Data/Image Pattern Reco...

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mathematics of data/image pattern recognition, compression, Coding, and encryption X, with applications

ISBN: (纸本)9780819468482

The proceedings contain 18 papers. The topics discussed include: model based compression of the calibration matrix for hyperspectral imaging systems;optimization of a lossless object-based compression embedded on GAIA a next-generation space telescope;design of multichannel filter banks for subband coding of audio signals using multirate signal processing techniques;the optimum running-type approximation for time-limited worst-case measures of error based on Fredholm integral equation using Pincherle-Goursat kernel;adaptive model and neural network based watermark identification;compressed versus uncompressed domain video watermarking;generation of lattice independent vector sets for pattern recognition applications;and the validity of pyramid k-means clustering.

关键词： pattern recognition

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