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

A summary of many of the new techniques developed in the last two decades for spectrum analysis of discrete time series is presented in this tutorial. An examination of the underlying time series model assumed by each technique serves as the common basis for understanding the differences among the various spectrum analysis approaches. Techniques discussed include the classical periodogram, classical Blackman-Tukey, autoregressive (maximum entropy), moving average, autotegressive-moving average, maximum likelihood, Prony, and Pisarenko methods. A summary table in the text provides a concise overview for all methods, including key references and appropriate equations for computation of each spectral estimate.

Spectrum analysis&#8212;A modern perspective

The recent advances in radio and em beddedsystem technologies have enabled the proliferation of wireless microsensor networks. Such wirelessly connected sensors are released in many diverse environments to perform various monitoring tasks. In many such tasks, location awareness is inherently one of the most essential system parameters. It is not only needed to report the origins of events, but also to assist group querying of sensors, routing, and to answer questions on the network coverage. In this paper we present a novel approach to the localization of sensors in an ad-hoc network. We describe a system called AHLoS (Ad-Hoc Localization System) that enables sensor nodes to discover their locations using a set distributed iterative algorithms. The operation of AHLoS is demonstrated with an accuracy of a few centimeters using our prototype testbed while scalability and performance are studied through simulation.

Dynamic fine-grained localization in Ad-Hoc networks of sensors

A vector quantizer is a system for mapping a sequence of continuous or discrete vectors into a digital sequence suitable for communication over or storage in a digital channel. The goal of such a system is data compression: to reduce the bit rate so as to minimize communication channel capacity or digital storage memory requirements while maintaining the necessary fidelity of the data. The mapping for each vector may or may not have memory in the sense of depending on past actions of the coder, just as in well established scalar techniques such as PCM, which has no memory, and predictive quantization, which does. Even though information theory implies that one can always obtain better performance by coding vectors instead of scalars, scalar quantizers have remained by far the most common data compression system because of their simplicity and good performance when the communication rate is sufficiently large. In addition, relatively few design techniques have existed for vector quantizers. During the past few years several design algorithms have been developed for a variety of vector quantizers and the performance of these codes has been studied for speech waveforms, speech linear predictive parameter vectors, images, and several simulated random processes. It is the purpose of this article to survey some of these design techniques and their applications.

Vector quantization

Information Theory and Reliable Communication

We consider strategies for the lossy transmission of a zero-mean memoryless Gaussian source over a 2times2 Rayleigh faded MIMO system with transmitter side information. The first strategy uses repetition coding, where the same symbol is duplicated over the two transmit antennas in two consecutive time slots. The second strategy employs the Alamouti scheme, while the third strategy is based on spatial multiplexing. A mean squared error distortion measure is assumed. Since this distortion results, at the receiver, in a continuous random variable, both its expected value and statistical distribution are used for evaluating the performance of each strategy. It is shown that the spatial multiplexing strategy achieves the lowest expected distortion and also that the utilization of the Alamouti strategy brings only a slight decrease in performance, but has the advantage of a lower realization complexity. Considering the statistical distributions of distortion, it is observed that increasing the signal-to-noise ratio results in higher probabilities of achieving the expected distortion and to less variable values of distortion. Due to the characteristics of the respective distributions of distortion, both Alamouti and spatial multiplexing strategies are capable of achieving low distortion with high probability while, at the same probability, the repetition strategy achieves significantly higher distortion.

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Source information transmission over MIMO systems with transmitter side information

: There are numrous operational requirements for remotely sensed imagery. The vast amounts of image data acquired in these applications pose a particular problem for transmission to other locations. In order to achieve real-time operation and to conserve communications channel bandwidth, it is common to compress the image data. Suitable image compression techniques reduce the required transmitted data rate by a factor of approximately 8-to-1, while still maintaining acceptable image quality. Unfortunately, when an image is compressed, it is much more susceptible to bit errors introduced by noisy channels. This report document research concerning image compression at 1 bit/pel over noisy channels. The techniques of bit weighting, soft decision demodulation, and channel coding are compared for reducing or eliminating the effects of channel errors on compressed imagery. The source coding method used is the two-dimensional discrete cosine transform of 16 by 16 blocks. Results indicate that for an independent bit error rate of 0.01, bit weighting alone and soft decision alone provide a noticeable improvement in reconstructed image quality. Combined bit weighting/soft decision outperforms either technique alone. Judicious channel coding can substantially improve image quality over noisy channels with only a slight degradation in ideal channel performance.

Bit Weighting and Soft Decision Demodulation for Image Compression & Error Control.

We generate nonuniform quantization parameter (QP) matrices to improve the perceptual quality of reconstructed video in the AVC/H.264 standard. The resulting 9 QP matrices are indexed to the 9 intra-frame prediction modes of the 4 times 4 blocks and can be applied to the 16 times 16 macroblock (MB)s which engage only the first 4 out of the 9 intra-modes. We also studied how the nonuniform QP matrices are affected by the local luminance level and the video frame rate. Two subjective experiments exploiting the luminance, texture, and temporal masking properties of the human vision system are conducted to generate the results. A third subjective experiment is conducted to evaluate the derived scheme. The data we collect from this experiment show that using the QP matrices instead of a uniform QP considerably improves the perceptual quality of AVC/H.264 compressed video sequences. Some implementation details are discussed

Jerry D. Gibson

Papers

Source information transmission over MIMO systems with transmitter side information

Bit Weighting and Soft Decision Demodulation for Image Compression & Error Control.

Intra-Mode Indexed Nonuniform Quantization Parameter Matrices in AVC/H.264

Backward adaptive predictor coefficient identification in ADPCM with robust quantization and PCQ

Conference Record - Asilomar Conference on Signals, Systems and Computers: Foreword