Feature Detection and Tracking

Abstract

Many textbooks on computer vision can be unwieldy and intimidatingin their coverage of this extensive discipline. This textbook addressesthe need for a concise overview of the fundamentals of this field.Concise Computer Vision provides an accessible general introductionto the essential topics in computer vision, highlighting the roleof important algorithms and mathematical concepts. Classroom-testedprogramming exercises and review questions are also supplied at theend of each chapter.Topics and features:Provides an introduction to the basic notation and mathematical conceptsfor describing an image, and the key concepts for mapping an imageinto an imageExplains the topologic and geometric basics for analysing image regionsand distributions of image values, and discusses identifying patternsin an imageIntroduces optic flow for representing dense motion, and such topicsin sparse motion analysis as keypoint detection and descriptor definition,and feature tracking using the Kalman filterDescribes special approaches for image binarization and segmentationof still images or video framesExamines the three basic components of a computer vision system, namelycamera geometry and photometry, coordinate systems, and camera calibrationReviews different techniques for vision-based 3D shape reconstruction,including the use of structured lighting, stereo vision, and shading-basedshape understandingIncludes a discussion of stereo matchers, and the phase-congruencymodel for image featuresPresents an introduction into classification and learning, with adetailed description of basic AdaBoost and the use of random forestsThis concise and easy to read textbook/reference is ideal for an introductorycourse at third- or fourth-year level in an undergraduate computerscience or engineering programme.