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 scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

Simultaneous sparse coding (SSC) or nonlocal image representation has shown great potential in various low-level vision tasks, leading to several state-of-the-art image restoration techniques, including BM3D and LSSC. However, it still lacks a physically plausible explanation about why SSC is a better model than conventional sparse coding for the class of natural images. Meanwhile, the problem of sparsity optimization, especially when tangled with dictionary learning, is computationally difficult to solve. In this paper, we take a low-rank approach toward SSC and provide a conceptually simple interpretation from a bilateral variance estimation perspective, namely that singular-value decomposition of similar packed patches can be viewed as pooling both local and nonlocal information for estimating signal variances. Such perspective inspires us to develop a new class of image restoration algorithms called spatially adaptive iterative singular-value thresholding (SAIST). For noise data, SAIST generalizes the celebrated BayesShrink from local to nonlocal models; for incomplete data, SAIST extends previous deterministic annealing-based solution to sparsity optimization through incorporating the idea of dictionary learning. In addition to conceptual simplicity and computational efficiency, SAIST has achieved highly competent (often better) objective performance compared to several state-of-the-art methods in image denoising and completion experiments. Our subjective quality results compare favorably with those obtained by existing techniques, especially at high noise levels and with a large amount of missing data.

Nonlocal Image Restoration With Bilateral Variance Estimation: A Low-Rank Approach

Thermal cameras are passive sensors that capture the infrared radiation emitted by all objects with a temperature above absolute zero. This type of camera was originally developed as a surveillance and night vision tool for the military, but recently the price has dropped, significantly opening up a broader field of applications. Deploying this type of sensor in vision systems eliminates the illumination problems of normal greyscale and RGB cameras. This survey provides an overview of the current applications of thermal cameras. Applications include animals, agriculture, buildings, gas detection, industrial, and military applications, as well as detection, tracking, and recognition of humans. Moreover, this survey describes the nature of thermal radiation and the technology of thermal cameras.

/pdf/thermal-cameras-and-applications-a-survey-4wd1xvg4uy.pdf

Thermal cameras and applications: a survey

This paper presents a robust occupancy analysis system for thermal imaging. Reliable detection of people is very hard in crowded scenes, due to occlusions and segmentation problems. We therefore propose a framework that optimises the occupancy analysis over long periods by including information on the transition in occupancy, when people enter or leave the monitored area. In stable periods, with no activity close to the borders, people are detected and counted which contributes to a weighted histogram. When activity close to the border is detected, local tracking is applied in order to identify a crossing. After a full sequence, the number of people during all periods are estimated using a probabilistic graph search optimisation. The system is tested on a total of 51,000 frames, captured in sports arenas. The mean error for a 30-minute period containing 3-13 people is 4.44 %, which is a half of the error percentage optained by detection only, and better than the results of comparable work. The framework is also tested on a public available dataset from an outdoor scene, which proves the generality of the method.

/pdf/long-term-occupancy-analysis-using-graph-based-optimisation-5c2nxwbzj3.pdf

Long-Term Occupancy Analysis Using Graph-Based Optimisation in Thermal Imagery

Pedestrian detection and tracking in infrared imagery using shape and appearance

This paper introduces a layered representation for infrared imagery and studies its application into pedestrian detection and tracking. We present a generalized EM algorithm to decompose infrared images into background and foreground layers and study the phenomenon of polarity switch. We propose a hybrid (shape+appearance) algorithm for pedestrian detection, in which shape cue is first used to eliminate non-pedestrian moving objects and appearance cue is then used to pin down the location of pedestrians. We also formulate the problem of shot segmentation and present a graph matching-based pedestrian tracking algorithm. Experimental results with OSU Thermal Pedestrian Database are reported to demonstrate the excellent performance of our algorithms.

Layered Representation for Pedestrian Detection and Tracking in Infrared Imagery

Methods and apparatus are provided for template matching prediction in video encoding and decoding. An apparatus includes an encoder (300) for encoding a target block in a picture by determining at least one predictor for the target block and respectively utilizing the at least one predictor as a template to search for at least one additional predictor for the target block.

Methods and apparatus for template matching prediction (tmp) in video encoding and decoding

In this paper, we present a patch-based variational Bayesian framework for video processing and demonstrate its potential in denoising, inpainting and deinterlacing. Unlike previous methods based on explicit motion estimation, we propose to embed motion-related information into the relationship among video patches and develop a nonlocal sparsity-based prior for typical video sequences. Specifically, we first extend block matching (nearest neighbor search) into patch clustering (k-nearest-neighbor search), which represents motion in an implicit and distributed fashion. Then we show how to exploit the sparsity constraint by sorting and packing similar patches, which can be better understood from a manifold perspective. Under the Bayesian framework, we treat both patch clustering result and unobservable data as latent variables and solve the inference problem via variational EM algorithms. A weighted averaging strategy of fusing diverse inference results from overlapped patches is also developed. The effectiveness of patch-based video models is demonstrated by extensive experimental results on a wide range of video materials.

Patch-Based Video Processing: A Variational Bayesian Approach

Modern video coding standards such as H.264/AVC use intra prediction for efficient coding of Intra pictures. These usually exploit local directional signal correlations. More recently, intra prediction modes using non-local signal information have been introduced. A very popular approach is the so called template matching prediction (TMP), which uses template based texture synthesis for signal prediction. This, combined with regular directional prediction, significantly improves intra coding efficiency compared to H.264/AVC. However, current TMP techniques have trouble synthesizing picture data with non-uniform illumination characteristics. They assume that similar picture regions resemble at the same time in structure and illumination, which is often not the case. In order to solve this, we propose a template matching technique with locally adaptive illumination compensation. The proposed technique is based on a linear compensation model with a scaling and an offset parameters to compensate for contrast and brightness disparities respectively. The model parameters are calculated by solving an auto-regressive Least Square problem during the template search for TMP. This permits to synthesize signal structures while capturing the local characteristics of illumination without needing extra side information. The total improvement in intra coding efficiency with respect to H.264/AVC can be of up to 18%.

Yunfei Zheng

Papers

Pedestrian detection and tracking in infrared imagery using shape and appearance

Layered Representation for Pedestrian Detection and Tracking in Infrared Imagery

Methods and apparatus for template matching prediction (tmp) in video encoding and decoding

Patch-Based Video Processing: A Variational Bayesian Approach

Intra prediction using template matching with adaptive illumination compensation