<p/><br></br><p><b> About the Book </b></p></br></br><p>Addressing the need for a concise and accessible introduction to the complex field of computer vision, this text reinforces its presentation of the essential topics with class-tested exercises. The coverage includes an historical overview of the technology.</p><p/><br></br><p><b> Book Synopsis </b></p></br></br><p>Many 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.</p><p><i>Concise Computer Vision</i> 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. </p><p><b></b></p>Topics 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.<p></p><p>This 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.</p><p/><br></br><p><b> From the Back Cover </b></p></br></br><p>Many 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.</p><p><i>Concise Computer Vision</i> 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.</p><p><b></b></p><b>Topics and features: </b><p></p><p><b></b></p><ul><li>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</li><li>Explains the topologic and geometric basics for analysing image regions and distributions of image values, and discusses identifying patterns in an image</li><li>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</li><li>Describes special approaches for image binarization and segmentation of still images or video frames</li><li>Examines the three basic components of a computer vision system, namely camera geometry and photometry, coordinate systems, and camera calibration</li><li>Reviews different techniques for vision-based 3D shape reconstruction, including the use of structured lighting, stereo vision, and shading-based shape understanding</li><li>Includes a discussion of stereo matchers, and the phase-congruency model for image features</li><li>Presents an introduction into classification and learning, with a detailed description of basic AdaBoost and the use of random forests</li></ul><p></p><p>This 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.</p><p/><br></br><p><b> About the Author </b></p></br></br><b>Dr. Reinhard Klette</b>, FRSNZ, is a Professor at the Tamaki Innovation Campus of The University of Auckland, New Zealand. His numerous other publications include the Springer title <i>Euclidean Shortest Paths: Exact or Approximate Algorithms</i>.
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