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检索条件"任意字段=Mathematics of Data/Image Pattern Coding, Compression, and Encryption with Applications XIII"

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Noise Tolerant Dendritic Lattice Associative Memories

Noise Tolerant Dendritic Lattice Associative Memories

引用

Conference on mathematics of data/image pattern coding, compression, and encryption with applications xiii

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

ISBN: (纸本)9780819487469

Linear classifiers based on computation over the real numbers R (e. g., with operations of addition and multiplication) denoted by (R, +, x), have been represented extensively in the literature of pattern recognition. However, a different approach to pattern classification involves the use of addition, maximum, and minimum operations over the reals in the algebra (R, +, v, Lambda). These pattern classifiers, based on lattice algebra, have been shown to exhibit superior information storage capacity, fast training and short convergence times, high pattern classification accuracy, and low computational cost. Such attributes are not always found, for example, in classical neural nets based on the linear inner product. In a special type of lattice associative memory (LAM), called a dendritic LAM or DLAM, it is possible to achieve noise-tolerant pattern classification by varying the design of noise or error acceptance bounds. This paper presents theory and algorithmic approaches for the computation of noise-tolerant lattice associative memories (LAMs) under a variety of input constraints. Of particular interest are the classification of nonergodic data in noise regimes with time-varying statistics. DLAMs, which are a specialization of LAMs derived from concepts of biological neural networks, have successfully been applied to pattern classification from hyperspectral remote sensing data, as well as spatial object recognition from digital imagery. The authors' recent research in the development of DLAMs is overviewed, with experimental results that show utility for a wide variety of pattern classification applications. Performance results are presented in terms of measured computational cost, noise tolerance, classification accuracy, and throughput for a variety of input data and noise levels.

关键词： pattern recognition Adaptive pattern classification Neural networks

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image registration error analysis using pattern recognition algorithms

Image registration error analysis using pattern recognition ...

引用

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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Massively Parallel Computation of Lattice Associative Memory Classifiers on Multicore Processors

Massively Parallel Computation of Lattice Associative Memory...

引用

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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Algorithms for Adaptive Nonlinear pattern Recognition

Algorithms for Adaptive Nonlinear Pattern Recognition

引用

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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Implementation IDEA algorithm for image encryption

Implementation IDEA algorithm for image encryption

引用

Conference on mathematics and applications of data/image coding, compression, and encryption III

作者： Dang, PP Chau, PM Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

ISBN: (纸本)0819437670

In this paper, we present an implementation of the IDEA algorithm for image encryption. The image encryption is incorporated into the compression algorithm for transmission over a data network, in the proposed method, Embedded Wavelet Zero-tree coding is used for image compression. Experimental results show that our proposed scheme enhances data security and reduces the network bandwidth required for video transmissions. A software implementation and system architecture for hardware implementation of the IDEA image encryption algorithm based on Field Programmable Gate Array (FPGA) technology are presented in this paper.

关键词： image encryption encryption decryption IDEA FPGA reconfigurable processor

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Generation of lattice independent vector sets for pattern recognition applications

Generation of lattice independent vector sets for pattern re...

引用

Conference on mathematics of data/image pattern Recongnition, compression, coding, and encryption X, with applications

作者： Urcid-S., Gonzalo Valdiviezo-N., Juan-Carlos INAOE Dept Opt Puebla 72000 Mexico

ISBN: (纸本)9780819468482

Lattice independence and strong lattice independence of a set of pattern vectors are fundamental mathematical proper-ties that lie at the core of pattern recognition applications based on lattice theory. Specifically, the development of morphological associative memories robust to inputs corrupted by random noise are based on strong lattice independent sets, and real world problems, such as, autonomous endmember detection in hyperspectral imagery, use auto-associative morphological memories as detectors of lattice independence. In this paper, we present a unified mathematical framework that-develops the relationship between different notions of lattice independence currently used in the literature. Computational procedures are provided to test if a given set of pattern vectors is lattice independent or strongly lattice independent;in addition, different techniques are fully described that can be used to generate sets of vectors with the aforementioned lattice properties.

关键词： lattice associative memories lattice independence morphological neural networks

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Benchmarking techniques for evaluation of compression transform performance in ATR applications

Benchmarking techniques for evaluation of compression transf...

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7th Conference on mathematics of data/image coding, compression, and encryption

作者： Schmalz, MS Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL 32611 USA

ISBN: (纸本)0819454990

image compression is increasingly employed in applications such as medical imaging, for reducing data storage requirement, and Internet video transmission, to effectively increase channel bandwidth. Similarly, military applications such as automated target recognition (ATR) often employ compression to achieve storage and communication efficiencies, particularly to enhance the effective bandwidth of communication channels whose throughput suffers, for example, from overhead due to error correction/detection or encryption. In the majority of cases, lossy compression is employed due the resultant low bit rates (high compression ratio). However, lossy compression produces artifacts in decompressed imagery that can confound ATR processes applied to such imagery, thereby reducing the probability of detection (Pd) and possibly increasing the rate or number of false alarms (Rfa or Nfa). In this paper, the authors' previous research in performance measurement of compression transforms is extended to include (a) benchmarking algorithms and software tools, (b) a suite of error exemplars that are designed to elicit compression transform behavior in an operationally relevant context, and (c) a posteriori analysis of performance data. The following transforms are applied to a suite of 64 error exemplars: Visual pattern image coding (VPIC [1]), Vector Quantization with a fast codebook search algorithm (VQ [2,3]), JPEG and a preliminary implementation of JPEG 2000 [4,5], and EBLAST [6-8]. compression ratios range from 2:1 to 200:1, and various noise levels and types are added to the error exemplars to produce a database of 7,680 synthetic test images. Several global and local (e.g., featural) distortion measures are applied to the decompressed test imagery to provide a basis for rate-distortion and rate-performance analysis as a function of noise and compression transform type.

关键词： image compression error measurement automated target recognition

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data encryption scheme with extended artithmetic coding

Data encryption scheme with extended artithmetic coding

引用

4th Conference on mathematics of image and data coding, compression, and encryption

作者： Ishibashi, H Tanaka, K Shinshu Univ Fac Engn Nagano 3808553 Japan

ISBN: (纸本)0819441899

In this work, we extend arithmetic coding and present a data encryption scheme that achieves data compression and data security at the same time. This scheme is based on a chaotic dynamics, which makes use of the fact that the decoding process of arithmetic coding scheme can be considered as the repetition of Bernoulli shift map. data encryption is achieved by controlling the piecewise linear maps by a secret key in three kinds of approach: (i) perturbation method, (ii) switching method, and (iii) source extension method. Experimental results show that the obtained arithmetic codes for a message are randomly distributed on the mapping domain [0,1) by using different keys without seriously deteriorating the compression ratio, and the transition of the orbits in the domain [0,1) is similar to the chaotic dynamics.

关键词： arithmetic coding data encryption data compression chaotic dynamical system pieceivise linear map

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Recovery of 3-D human posture from double views

Recovery of 3-D human posture from double views

引用

Conference on mathematics of data/image coding, compression, and encryption VI, with applications

作者： Sun, Y Li, MH He, YH Dalian Univ Technol Sch Elect & Informat Engn Dalian 116024 Peoples R China

ISBN: (纸本)0819450812

The 3-D estimation of human motion captured by a video camera(s) is becoming a challenging scientific task. In this C, paper, two important problems are addressed: the acquisition of 3-D coordinates of human's body parts, and the key-frame-based human motion prediction. To the key frames, a fuzzy pattern matching method is adopted to detect and get 2-D image coordinates of the face in each image sequence. After stereo matching of the same feature point, we use a binocular imaging model to accomplish the acquisition of 3-D coordinates of the head. Out of the key frames, we use a Kalman estimator to predict the motion of human body parts. At the end of the paper, the experimental result and discussion are given.

关键词： key frame fuzzy pattern matching stereo imaging Kalman filter prediction

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Combined data encryption and compression using chaos functions

Combined data encryption and compression using chaos functio...

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7th Conference on mathematics of data/image coding, compression, and encryption

作者： Bose, R Pathak, S Indian Inst Technol Dept Elect Engn New Delhi 110016 India

ISBN: (纸本)0819454990

Past research in the field of cryptography has not given much consideration to arithmetic coding as a feasible encryption technique, with studies proving compression-specific arithmetic. coding to be largely unsuitable for encryption. Nevertheless, adaptive modelling, which offers a huge model, variable in structure, and as completely as possible a function of the entire text that has been transmitted since the time the model was initialised, is a suitable candidate for a possible encryption-compression combine. The focus of the work presented in this paper has been to incorporate recent results of chaos theory, proven to be cryptographically secure, into arithmetic coding, to devise a convenient method to make the structure of the model unpredictable and variable in nature, and yet to retain, as far as is possible, statistical harmony, so that compression is possible. A chaos-based adaptive arithmetic coding-encryption technique has been designed, developed and tested and its implementation has been discussed. For typical text files, the proposed encoder gives compression between 67.5% and 70.5%, the zero-order compression suffering by about 6% due to encryption, and is not susceptible to previously carried out attacks on arithmetic coding algorithms.

关键词： symmetric key cryptography variable model arithmetic coding chaos compression encryption

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