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020 _a9781447163206
_9978-1-4471-6320-6
024 7 _a10.1007/978-1-4471-6320-6
_2doi
050 4 _aTA1637-1638
050 4 _aTA1637-1638
072 7 _aUYT
_2bicssc
072 7 _aUYQV
_2bicssc
072 7 _aCOM012000
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072 7 _aCOM016000
_2bisacsh
082 0 4 _a006.6
_223
082 0 4 _a006.37
_223
100 1 _aKlette, Reinhard.
_eauthor.
245 1 0 _aConcise Computer Vision
_h[electronic resource] :
_bAn Introduction into Theory and Algorithms /
_cby Reinhard Klette.
264 1 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2014.
300 _aXVIII, 429 p. 298 illus., 229 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aUndergraduate Topics in Computer Science,
_x1863-7310
505 0 _aImage Data -- Image Processing -- Image Analysis -- Dense Motion Analysis -- Image Segmentation -- Cameras, Coordinates and Calibration -- 3D Shape Reconstruction -- Stereo Matching -- Feature Detection and Tracking -- Object Detection.
520 _aMany 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. Concise 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 questions are also supplied at the end of each chapter. 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 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.
650 0 _aComputer science.
650 0 _aElectronic data processing.
650 0 _aComputer vision.
650 1 4 _aComputer Science.
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aComputing Methodologies.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781447163190
830 0 _aUndergraduate Topics in Computer Science,
_x1863-7310
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4471-6320-6
912 _aZDB-2-SCS
999 _c91931
_d91931