Motion Analysis and Image Sequence Processing


Book Description

An image or video sequence is a series of two-dimensional (2-D) images sequen tially ordered in time. Image sequences can be acquired, for instance, by video, motion picture, X-ray, or acoustic cameras, or they can be synthetically gen erated by sequentially ordering 2-D still images as in computer graphics and animation. The use of image sequences in areas such as entertainment, visual communications, multimedia, education, medicine, surveillance, remote control, and scientific research is constantly growing as the use of television and video systems are becoming more and more common. The boosted interest in digital video for both consumer and professional products, along with the availability of fast processors and memory at reasonable costs, has been a major driving force behind this growth. Before we elaborate on the two major terms that appear in the title of this book, namely motion analysis and image sequence processing, we like to place them in their proper contexts within the range of possible operations that involve image sequences. In this book, we choose to classify these operations into three major categories, namely (i) image sequence processing, (ii) image sequence analysis, and (iii) visualization. The interrelationship among these three categories is pictorially described in Figure 1 below in the form of an "image sequence triangle".




Computer Vision Analysis of Image Motion by Variational Methods


Book Description

This book presents a unified view of image motion analysis under the variational framework. Variational methods, rooted in physics and mechanics, but appearing in many other domains, such as statistics, control, and computer vision, address a problem from an optimization standpoint, i.e., they formulate it as the optimization of an objective function or functional. The methods of image motion analysis described in this book use the calculus of variations to minimize (or maximize) an objective functional which transcribes all of the constraints that characterize the desired motion variables. The book addresses the four core subjects of motion analysis: Motion estimation, detection, tracking, and three-dimensional interpretation. Each topic is covered in a dedicated chapter. The presentation is prefaced by an introductory chapter which discusses the purpose of motion analysis. Further, a chapter is included which gives the basic tools and formulae related to curvature, Euler Lagrange equations, unconstrained descent optimization, and level sets, that the variational image motion processing methods use repeatedly in the book.










Stereo Scene Flow for 3D Motion Analysis


Book Description

This book presents methods for estimating optical flow and scene flow motion with high accuracy, focusing on the practical application of these methods in camera-based driver assistance systems. Clearly and logically structured, the book builds from basic themes to more advanced concepts, culminating in the development of a novel, accurate and robust optic flow method. Features: reviews the major advances in motion estimation and motion analysis, and the latest progress of dense optical flow algorithms; investigates the use of residual images for optical flow; examines methods for deriving motion from stereo image sequences; analyses the error characteristics for motion variables, and derives scene flow metrics for movement likelihood and velocity; introduces a framework for scene flow-based moving object detection and segmentation; includes Appendices on data terms and quadratic optimization, and scene flow implementation using Euler-Lagrange equations, in addition to a helpful Glossary.




Multilevel Optimization for Dense Motion Estimation (UUM Press)


Book Description

This monograph offers design for fast and reliable technique in the dense motion estimation. This Multilevel Optimization for Dense Motion Estimation work blends both theory and applications to equip reader with an understanding of basic concepts necessary to apply in solving dense motion in a sequence of images. Illustrating well-known variation models for dealing with optical flow estimation, this monograph introduces variation models with applications. A host of variation models are outlines such as Horn-Schunck model, Contrast Invariation Models and Models for Large Displacement. Special attention is also given to multilevel optimization techniques namely multiresolution and multigrid methods to improve the convergence of the global optimum when compared to using only one level resolution in the context of computer vision. This monograph is a robust resource that provides insightful introduction to the field of image processing with its theory and applications. Overall, Multilevel Optimization for Dense Motion Estimation is highly recommended for scientists and engineers for an excellent choice for references and self-study.







Semantic Video Object Segmentation for Content-Based Multimedia Applications


Book Description

Semantic Video Object Segmentation for Content-Based Multimedia Applications provides a thorough review of state-of-the-art techniques as well as describing several novel ideas and algorithms for semantic object extraction from image sequences. Semantic object extraction is an essential element in content-based multimedia services, such as the newly developed MPEG4 and MPEG7 standards. An interactive system called SIVOG (Smart Interactive Video Object Generation) is presented, which converts user's semantic input into a form that can be conveniently integrated with low-level video processing. Thus, high-level semantic information and low-level video features are integrated seamlessly into a smart segmentation system. A region and temporal adaptive algorithm was further proposed to improve the efficiency of the SIVOG system so that it is feasible to achieve nearly real-time video object segmentation with robust and accurate performances. Also included is an examination of the shape coding problem and the object segmentation problem simultaneously. Semantic Video Object Segmentation for Content-Based Multimedia Applications will be of great interest to research scientists and graduate-level students working in the area of content-based multimedia representation and applications and its related fields.




Robotic Vision: Technologies for Machine Learning and Vision Applications


Book Description

Robotic systems consist of object or scene recognition, vision-based motion control, vision-based mapping, and dense range sensing, and are used for identification and navigation. As these computer vision and robotic connections continue to develop, the benefits of vision technology including savings, improved quality, reliability, safety, and productivity are revealed. Robotic Vision: Technologies for Machine Learning and Vision Applications is a comprehensive collection which highlights a solid framework for understanding existing work and planning future research. This book includes current research on the fields of robotics, machine vision, image processing and pattern recognition that is important to applying machine vision methods in the real world.