Concise Computer Vision (Free)

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And the key concepts for an image into an 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 Oordinate systems and camera calibration; reviews different techniues for vision based 3D shape reconstruction including the use of structured ighting 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 earning with a detailed description of basic AdaBoost and the use of random forestsThis concise learning with a detailed description of basic AdaBoost and the use of random forestsThis concise easy to read textbookreference is ideal for an introductory course at third or fourth year evel in an undergraduate computer science or engineering programme.

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Many textbooks on computer vision Can Be Unwieldy be unwieldy and in their coverage of extensive discipline This textbook addresses the need for a concise overview of the fundamentals of this fieldConcise Computer Vision 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 uestions are also supplied at the end of each chapterTopics and features provides an introduction to the basic notation and mathematical concepts for describing an image. Concise Computer Vision