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

The PASCAL Visual Object Classes Challenge

The theory of affordances

Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables systematic development of optimal vision algorithms when used with optimization principles. This detailed and thoroughly enhanced third edition presents a comprehensive study / reference to theories, methodologies and recent developments in solving computer vision problems based on MRFs, statistics and optimisation. It treats various problems in low- and high-level computational vision in a systematic and unified way within the MAP-MRF framework. Among the main issues covered are: how to use MRFs to encode contextual constraints that are indispensable to image understanding; how to derive the objective function for the optimal solution to a problem; and how to design computational algorithms for finding an optimal solution. Easy-to-follow and coherent, the revised edition is accessible, includes the most recent advances, and has new and expanded sections on such topics as: Discriminative Random Fields (DRF) Strong Random Fields (SRF) Spatial-Temporal Models Total Variation Models Learning MRF for Classification (motivation + DRF) Relation to Graphic Models Graph Cuts Belief Propagation Features: Focuses on the application of Markov random fields to computer vision problems, such as image restoration and edge detection in the low-level domain, and object matching and recognition in the high-level domain Presents various vision models in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation Uses a variety of examples to illustrate how to convert a specific vision problem involving uncertainties and constraints into essentially an optimization problem under the MRF setting Introduces readers to the basic concepts, important models and various special classes of MRFs on the regular image lattice and MRFs on relational graphs derived from images Examines the problems of parameter estimation and function optimization Includes an extensive list of references This broad-ranging and comprehensive volume is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It has been class-tested and is suitable as a textbook for advanced courses relating to these areas.

Markov Random Field Modeling in Image Analysis

https://hal.inria.fr/inria-00074398/document

A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry

With the services that autonomous robots are to provide becoming more demanding, the states that the robots have to estimate become more complex. In this paper, we develop and analyze a probabilistic, vision-based state estimation method for individual autonomous robots. This method enables a team of mobile robots to estimate their joint positions in a known environment and track the positions of autonomously moving objects. The state estimators of different robots cooperate to increase the accuracy and reliability of the estimation process. This cooperation between the robots enables them to track temporarily occluded objects and to faster recover their position after they have lost track of it. The method is empirically validated based on experiments with a team of physical robots.

Cooperative probabilistic state estimation for vision-based autonomous mobile robots

Great interest is recently shown in behavior modeling, especially in public surveillance tasks. In general it is agreed upon the benefits of use of several input cues as audio and video. Yet, synchronization and fusion of these information sources remains the main challenge. We therefore show results for a feature space combination, which allows for overall feature space optimization. Audio and video features are thereby firstly derived as low-level-descriptors. Synchronization and feature combination is achieved by multivariate time-series analysis. Test-runs on a database of aggressive, cheerful, intoxicated, nervous, neutral, and tired behavior in an airplane situation show a significant improvement over each single modality.

https://mediatum.ub.tum.de/doc/1138565/file.pdf

Audiovisual Behavior Modeling by Combined Feature Spaces

Image sequence analysis using relational structures

An approach to complete road networks extracted from aerial images is presented. Since low-level road extraction results will generally be fragmented and contain false hypotheses, this task involves selecting the correct roads from the extraction results as well as connecting them to construct the road network. Since the presented approach models the road network as a graph, it can handle different kinds of low-level representations, e.g., road segments given by lines or polygons. A flexible scheme to evaluate the quality of the connection hypotheses based on fuzzy set theory is introduced. The problem of connecting the road network is reduced to selecting a few salient nodes in the graph and finding an optimal path between them. Salient nodes are, for instance, the points where a road crosses the image border or points at the ends of very long road segments. Practical results show the validity of the approach.

The Role of Grouping for Road Extraction

In this paper, the impact of facial expressions on HRI is explored. To determine their influence on empathy of a human towards a robot and perceived subjective performance, an experimental setup is created, in which participants engage in a dialog with the robot head EDDIE. The web-based gaming application “Akinator” serves as a backbone for the dialog structure. In this game, the robot tries to guess a thought-of person chosen by the human by asking various questions about the person. In our experimental evaluation, the robot reacts in various ways to the human's facial expressions, either ignoring them, mirroring them, or displaying its own facial expression based on a psychological model for social awareness. In which way this robot behavior influences human perception of the interaction is investigated by a questionnaire. Our results support the hypothesis that the robot behavior during interaction heavily influences the extent of empathy by a human towards a robot and perceived subjective task-performance, with the adaptive modes clearly leading compared to the non-adaptive mode.

Bernd Radig

Papers

Cooperative probabilistic state estimation for vision-based autonomous mobile robots

Audiovisual Behavior Modeling by Combined Feature Spaces

Image sequence analysis using relational structures

The Role of Grouping for Road Extraction

Improving aspects of empathy and subjective performance for HRI through mirroring facial expressions