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Machine learning in image steganalysis /

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2012年

作者： Schaathun Hans Georg.

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The Research on pattern recognition in Distributed Fiber Vibrant Sensor System

The Research on Pattern Recognition in Distributed Fiber Vib...

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

作者： Wu, Hongyan Zhao, Dong Xu, Haiyan Fudan Univ Dept Mat Sci Shanghai 200433 Peoples R China HoHai Univ Dept Comp & Informat Changzhou 213022 Peoples R China

ISBN: (纸本)9780819487469

Distributed Fiber Vibrant Sensor System is a new type of system, which could be used in long-distance, strong-EMI condition for monitoring vibration and sound signals. Position determination analysis toward this system is popular in previous papers, but pattern recognition of the output signals of the sensor has been missed for a long time. This function turns to critical especially when it is used for real security project in which quick response to intrusion is a must. After pre-processing the output signal of the system, a MFCC-based approach is provided in this paper to extract features of the sensing signals, which could be used for pattern recognition in real project, and the approach is proved by large practical experiments and projects.

关键词： fiber vibrant sensor pattern recognition MFCC

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Noise Tolerant Dendritic Lattice Associative Memories

Noise Tolerant Dendritic Lattice Associative Memories

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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 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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Algorithms for Adaptive Nonlinear pattern recognition

Algorithms for Adaptive Nonlinear Pattern Recognition

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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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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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应用数学促信息化应用深入发展

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软件产业与工程 2011年第3期 32-35页

作者：夏天翊美国伊利诺伊大学厄本那香槟分校厄本那香槟61801

本文构建了应用数学促进信息化应用的关系框架,重点阐述了未来应用数学在信息化领域中的四大重要应用方向,包括网络技术、图像处理以及模式识别、数据加密和身份认证、数据挖掘技术,得出应用数学与信息化结合的作用结果,包括利用应用数... 详细信息

本文构建了应用数学促进信息化应用的关系框架,重点阐述了未来应用数学在信息化领域中的四大重要应用方向,包括网络技术、图像处理以及模式识别、数据加密和身份认证、数据挖掘技术,得出应用数学与信息化结合的作用结果,包括利用应用数学工具扩大信息化应用广度、深度,并对现有应用发展进行支持和优化,提高信息化的价值,支持决策分析。

关键词：应用数学信息化应用方向学科结合

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Embedded zeroblock coding algorithm based on KLT and wavelet transform for hyperspectral image compression

Embedded zeroblock coding algorithm based on KLT and wavelet...

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MIPPR 2009 - Remote Sensing and GIS data Processing and Other applications: 6th International Symposium on Multispectral image Processing and pattern recognition

作者： Hou, Ying School of Communication and Information Engineering Xi'an Univ. of Science and Technology 710054 Xi'an China

ISBN: (纸本)9780819478092

In this paper, a hyperspectral image lossy coder using three-dimensional Embedded ZeroBlock Coding (3D EZBC) algorithm based on Karhunen-Loève transform (KLT) and wavelet transform (WT) is proposed. This coding scheme adopts 1D KLT as spectral decorrelator and 2D WT as spatial decorrelator. Furthermore, the computational complexity and the coding performance of the low-complexity KLT are compared and evaluated. In comparison with several stateof- the-art coding algorithms, experimental results indicate that our coder can achieve better lossy compression performance. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： image compression

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