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

From the Publisher:
Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms systematically when used with optimization principles. This book presents a comprehensive study on the use of MRFs for solving computer vision problems. The book covers the following parts essential to the subject: introduction to fundamental theories, formulations of MRF vision models, MRF parameter estimation, and optimization algorithms. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This book is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition, and applications of MRFs. It is also suitable as a text for advanced courses in these areas.

Markov Random Field Modeling in Computer Vision

The authors review major recent developments in establishing stereo correspondence for the extraction of the 3D structure of a scene. Broad categories of stereo algorithms are identified on the basis of differences in imaging geometry, matching primitives, and the computational structure used. The performance of these stereo techniques on various classes of test images is reviewed, and possible directions of future research are indicated. >

Structure from stereo-a review

Multielement Visual Tracking: Attention and Perceptual Organization

The authors present a closed-form, physically based solution for recovering a three-dimensional (3-D) solid model from collections of 3-D surface measurements. Given a sufficient number of independent measurements, the solution is overconstrained and unique except for rotational symmetries. The proposed approach is based on the finite element method (FEM) and parametric solid modeling using implicit functions. This approach provides both the convenience of parametric modeling and the expressiveness of the physically based mesh formulation and, in addition, can provide great accuracy at physical simulation. A physically based object-recognition method that allows simple, closed-form comparisons of recovered 3-D solid models is presented. The performance of these methods is evaluated using both synthetic range data with various signal-to-noise ratios and using laser rangefinder data. >

Closed-form solutions for physically based shape modeling and recognition

The authors describe an approach to perceptual grouping for detecting and describing 3-D objects in complex images and apply it to the task of detecting and describing complex buildings in aerial images. They argue that representations of structural relationships in the arrangements of primitive image features, as detected by the perceptual organization process, are essential for analyzing complex imagery. They term these representations collated features. The choice of collated features is determined by the generic shape of the desired objects in the scene. The detection process for collated features is more robust than the local operations for region segmentation and contour tracing. The important structural information encoded in collated features aids various visual tasks such as object segmentation, correspondence processes, and shape description. The proposed method initially detects all reasonable feature groupings. A constraint satisfaction network is then used to model the complex interactions between the collations and select the promising ones. Stereo matching is performed on the collations to obtain height information. This aids in further reasoning on the collated features and results in the 3-D description of the desired objects. >

Using perceptual organization to extract 3D structures

An algorithm is presented for error detection and correction of disparity, as a process separate from stereo matching, with the contention that matching is not necessarily the best way to utilize all the physical constraints characteristic to stereopsis As a result of the bias in stereo research towards matching, vision tasks like surface interpolation and object modeling have to accept erroneous data from the stereo matchers without the benefits of any intervening stage of error correction An algorithm which identifies all errors in disparity data that can be detected on the basis of figural continuity and corrects them is presented The algorithm can be used as a postprocessor to any edged-based stereo matching algorithm, and can additionally be used to automatically provide quantitative evaluations on the performance of matching algorithms of this class >

R. Mohan

Papers

Using perceptual organization to extract 3D structures

Stereo error detection, correction, and evaluation