u003cpu003eMany textbooks on computer vision can be unwieldy and intimidating in their coverage of this extensive discipline. This textbook addresses the need for a concise overview of the fundamentals of this field.u003c/pu003eu003cpu003eu003ciu003eConcise Computer Visionu003c/iu003e provides an accessible general introduction to the essential topics in computer vision, highlighting the role of important algorithms and mathematical concepts. Classroom-tested programming exercises and review questions are also supplied at the end of each chapter. u003c/pu003eu003cpu003eu003cbu003eu003c/bu003eu003c/pu003eTopics and features: provides an introduction to the basic notation and mathematical concepts for describing an image, and the key concepts for mapping an image into an image; explains the topologic and geometric basics for analysing image regions and distributions of image values, and discusses identifying patterns in an image; introduces optic flow for representing dense motion, and such topics in sparse motion analysis as keypoint detection and descriptor definition, and feature tracking using the Kalman filter; describes special approaches for image binarization and segmentation of still images or video frames; examines the three basic components of a computer vision system, namely camera geometry and photometry, coordinate systems, and camera calibration; reviews different techniques for vision-based 3D shape reconstruction, including the use of structured lighting, stereo vision, and shading-based shape understanding; includes a discussion of stereo matchers, and the phase-congruency model for image features; presents an introduction into classification and learning, with a detailed description of basic AdaBoost and the use of random forests.u003cpu003eu003c/pu003eu003cpu003eThis concise and easy to read textbook/reference is ideal for an introductory course at third- or fourth-year level in an undergraduate computer science or engineering programme.u003c/pu003e
