Introductory Techniques for 3-D Computer Vision
Senior/Graduate level courses on computer vision, robot vision and image processing in electrical and computer engineering, mathematics, and computer science departments, and an essential reference for researchers and scientists in the field of computer vision. An applied introduction to modern computer vision, focusing on a set of computational techniques for 3-D imaging. Covers a wide range of fundamental problems encountered within computer vision and provides detailed algorithmic and theoretical solutions for each. Each chapter concentrates on a specific problem and solves it by building on previous results.
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Dealing with Image Noise
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3-D objects 3-D points algorithm Analysis and Machine angle Appendix assume assumptions calibration pattern camera model camera reference frame Chapter components computer vision constraints contours coordinates corresponding cross-ratio curvature curve defined derivatives descriptors detection digital images discussion distance edge detector eigenspace eigenvalues ellipse epipolar geometry epipole equations essential matrix estimate extrinsic feature-based Figure focal length Further Readings Gaussian geometric given identified IEEE Transactions image brightness image center image features image gradient image lines image plane image points Image Processing implementation input intensity images intrinsic parameters invariants iteration Kalman filter kernel least squares linear matching measurements method motion field noise normal Notice object models obtained optical flow output pair patches pixel planar Problem Statement projection matrix projective transformation range images recognition reconstruction reference frame reflectance map rotation matrix sampling segments shape from shading singular value solution solve spatial stereo system surface translation vector weak-perspective