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

A significance map is a representation of positions of significant transform coefficients. In adaptive coding schemes, this map changes with the sample functions and a large fraction of code bits are used to encode it in low bit rate situations. In this paper, using the component variance as significance measure, a coding scheme is designed to reduce the number of code bits used for the significance map. Although it is a suboptimal approach, the performance is close to the rate distortion limit at a low bit rate. The result also justifies some earlier adaptive schemes using spectral entropy as an indicator of the number of coefficients to be coded.

Coefficient rate and significance maps in transform coding

The communication of voice over wireless local area networks (WLANs) is influenced by the choice of speech codec, packetization interval and PHY layer bit rates. These choices affect the number of voice users that can be supported on the WLAN as well as the speech quality experienced by each user. We investigate the effect of different combinations of these parameters for a 802.11a WLAN in different channel conditions with the objective of maximizing the number of voice users supported on the WLAN subject to a quality constraint. We use an indicator for assessing the speech quality experienced by a single user in a WLAN, based on a Perceptual Evaluation of Speech Quality (PESQ) Mean Opinion Score (MOS) constraint and the probability of a voice user achieving this constraint. The contributions of this paper are three-fold. First, a PHY layer rate adaptation scheme is proposed, in which the operating rate for each Signal to Noise Ratio (SNR) is chosen as the one that maximizes the capacity given a quality constraint. Second, based on the PHY layer rate adaptation scheme, we evaluate the effect of the choice of codec and voice payload size on the capacity values obtainable at different SNRs. Finally, we show the effect of channel conditions and the tightening of quality constraints on the capacity values at different SNRs.

/pdf/voice-capacity-under-quality-constraints-for-ieee-802-11a-2yodxth5kv.pdf

Voice capacity under quality constraints for IEEE 802.11a based WLANs

We revisit the classic problem of developing a spatial correlation model for natural images and videos by proposing a conditional correlation model for relatively nearby pixels that is dependent upon five parameters. The conditioning is on local texture and the optimal parameters can be calculated for a specific image or video with a mean absolute error usually smaller than 5%. We use this conditional correlation model to calculate the conditional rate distortion function when universal side information on local texture is available at both the encoder and the decoder. We demonstrate that this side information, when available, can save as much as 1 bit per pixel for selected videos at low distortions. We further study the scenario when the video frame is processed in macroblocks (MBs) or smaller blocks and calculate the rate distortion bound when the texture information is coded losslessly and optimal predictive coding is utilized to partially incorporate the correlation between the neighboring MBs or blocks. These rate distortion bounds are compared to the operational rate distortion functions generated in intra-frame coding using the H.264/AVC video coding standard.

New Rate Distortion Bounds for Natural Videos Based on a Texture-Dependent Correlation Model

We design a video encoding scheme that is suited for applications such as unmanned aerial vehicle (UAV) video surveillance where the encoder complexity needs to be low. Our low complexity encoder predicts frames using the global motion information available in UAVs and thus achieves lower complexity and more than 40% BD-rate savings for fly-over videos compared to a complexity-constrained H.264 encoder with motion estimation restricted to 8 $\times$ 8 blocks and half pixel accuracy. We also incorporate a spectral entropy based bit allocation scheme into this encoder to achieve near constant quality within groups of pictures (GOPs) at the cost of small increases in delay and complexity, and a small drop in compression efficiency. Both these encoders with their corresponding low complexity “matched” decoders provide significant gains of more than 49% BD-rate savings over the Wyner-Ziv based DISCOVER codec which has a low complexity encoder and a high complexity decoder. Furthermore, for videos where the global motion is spatially consistent within 2 $\times$ 2 blocks, we show that the computational complexity of these proposed encoders can be significantly reduced with only about 1% BD-rate increase.

Global Motion Assisted Low Complexity Video Encoding for UAV Applications

Digital transmission of uncompressed high-definition video is challenging because of its high data rate and its extreme sensitivity to bit errors. In this paper we propose a simple error correction scheme to reduce the bit error effects in the video at the receiver end of a wireless channel. Our scheme uses the large amount of spatial redundancy already present in uncompressed HD video data to provide an extra layer of protection in addition to that provided by channel coding. Thus, our method requires no change to the video signal being transmitted and is compatible with any existing solution for HD video transmission. Using simulations over a range of byte error rates, we show that our scheme effectively reduces the number of visible artifacts because of uncorrected channel errors, and provides approximately 7 dB improvement in peak signal to noise ratio.

/pdf/error-correction-scheme-for-uncompressed-hd-video-over-yadyninsi3.pdf

Jerry D. Gibson

Papers

Coefficient rate and significance maps in transform coding

Voice capacity under quality constraints for IEEE 802.11a based WLANs

New Rate Distortion Bounds for Natural Videos Based on a Texture-Dependent Correlation Model

Global Motion Assisted Low Complexity Video Encoding for UAV Applications

Error correction scheme for uncompressed HD video over wireless