From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

1 Introduction to Wavelets 2 A Multiresolution Formulation of Wavelet Systems 3 Filter Banks and the Discrete Wavelet Transform 4 Bases, Orthogonal Bases, Biorthogonal Bases, Frames, Tight Frames, and Unconditional Bases 5 The Scaling Function and Scaling Coefficients, Wavelet and Wavelet Coefficients 6 Regularity, Moments, and Wavelet System Design 7 Generalizations of the Basic Multiresolution Wavelet System 8 Filter Banks and Transmultiplexers 9 Calculation of the Discrete Wavelet Transform 10 Wavelet-Based Signal Processing and Applications 11 Summary Overview 12 References Bibliography Appendix A Derivations for Chapter 5 on Scaling Functions Appendix B Derivations for Section on Properties Appendix C Matlab Programs Index

Introduction to Wavelets and Wavelet Transforms: A Primer

Sphere Packings, Lattices and Groups

Quantization is a process that maps a continous or discrete set of values into approximations that belong to a smaller set. Quantization is a lossy: some information about the original data is lost in the process. The key to a successful quantization is therefore the selection of an error criterion – such as entropy and signal-to-noise ratio – and the development of optimal quantizers for this criterion.

Quantization

An overview of statistical and information-theoretic aspects of hidden Markov processes (HMPs) is presented. An HMP is a discrete-time finite-state homogeneous Markov chain observed through a discrete-time memoryless invariant channel. In recent years, the work of Baum and Petrie (1966) on finite-state finite-alphabet HMPs was expanded to HMPs with finite as well as continuous state spaces and a general alphabet. In particular, statistical properties and ergodic theorems for relative entropy densities of HMPs were developed. Consistency and asymptotic normality of the maximum-likelihood (ML) parameter estimator were proved under some mild conditions. Similar results were established for switching autoregressive processes. These processes generalize HMPs. New algorithms were developed for estimating the state, parameter, and order of an HMP, for universal coding and classification of HMPs, and for universal decoding of hidden Markov channels. These and other related topics are reviewed.

http://www.isib.cnr.it/control/finesso/Ephraim_hmm.pdf

Hidden Markov processes

This letter explains the effect of graph connectivity on error-floor performance of low-density parity-check (LDPC) codes under message-passing decoding A new metric, called extrinsic message degree (EMD), measures cycle connectivity in bipartite graphs of LDPC codes Using an easily computed estimate of EMD, we propose a Viterbi-like algorithm that selectively avoids small cycle clusters that are isolated from the rest of the graph This algorithm is different from conventional girth conditioning by emphasizing the connectivity as well as the length of cycles The algorithm yields codes with error floors that are orders of magnitude below those of random codes with very small degradation in capacity-approaching capability

Selective avoidance of cycles in irregular LDPC code construction

We describe a new image coding approach in which a 4-ary arithmetic coder is used to represent significant coefficient values and the lengths of zero runs between coefficients. This algorithm works by raster scanning within subbands, and therefore involves much lower addressing complexity than other algorithms such as zerotree coding that require the creation and maintenance of lists of dependencies across different decomposition levels. Despite its simplicity, and the fact that these dependencies are not explicitly utilized, the algorithm presented here is competitive with the best enhancements of zerotree coding. In addition, it performs comparably with adaptive subband splitting approaches that involve much higher implementation complexity.

Stack-run image coding

Although arithmetic coding offers extremely high coding efficiency, it provides little or no security as traditionally implemented. We present a modified scheme that offers both encryption and compression. The system utilizes an arithmetic coder in which the overall length within the range [0,1) allocated to each symbol is preserved, but the traditional assumption that a single contiguous interval is used for each symbol is removed. Additionally, a series of permutations are applied at the input and the output of the encoder. The overall system provides simultaneous encryption and compression, with negligible coding efficiency penalty relative to a traditional arithmetic coder

Secure Arithmetic Coding

Binary arithmetic coding involves recursive partitioning the range [0,1) in accordance with the relative probabilities of occurrence of the two input symbols. We describe a modification of this approach in which the overall length within the range [0,1) allocated to each symbol is preserved, but the traditional assumption that a single contiguous interval is used for each symbol is removed. A key known to both the encoder and decoder is used to describe where the intervals are "split" prior to encoding each new symbol. The repeated splitting has the effect of both scrambling the intervals and altering their lengths, thereby allowing both encryption and compression to be obtained simultaneously.

Binary arithmetic coding with key-based interval splitting

We describe a joint source-channel scheme for modifying a turbo decoder in order to exploit the statistical characteristics of hidden Markov sources. The basic idea is to treat the trellis describing the hidden Markov source as another constituent decoder which exchanges information with the other constituent decoder blocks. The source block uses as extrinsic information the probability of the input bits that is provided by the constituent decoder blocks. On the other hand, it produces a new estimation of such a probability which will be used as extrinsic information by the constituent turbo decoders. The proposed joint source-channel decoding technique leads to significantly improved performance relative to systems in which source statistics are not exploited and avoids the need to perform any explicit source coding prior to transmission. Lack of a priori knowledge of the source parameters does not degrade the performance of the system, since these parameters can be jointly estimated with turbo decoding.

J.D. Villasenor

Papers

Selective avoidance of cycles in irregular LDPC code construction

Stack-run image coding

Secure Arithmetic Coding

Binary arithmetic coding with key-based interval splitting

Joint turbo decoding and estimation of hidden Markov sources