LEADER 05344nam 22006975 450 001 9910255017803321 005 20200704103407.0 010 $a3-319-46364-0 024 7 $a10.1007/978-3-319-46364-3 035 $a(CKB)4340000000024217 035 $a(DE-He213)978-3-319-46364-3 035 $a(MiAaPQ)EBC4765986 035 $a(PPN)197455751 035 $a(EXLCZ)994340000000024217 100 $a20161206d2016 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aOptimization Techniques in Computer Vision $eIll-Posed Problems and Regularization /$fby Mongi A. Abidi, Andrei V. Gribok, Joonki Paik 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (XV, 293 p. 127 illus., 23 illus. in color.) 225 1 $aAdvances in Computer Vision and Pattern Recognition,$x2191-6586 311 $a3-319-46363-2 320 $aIncludes bibliographical references and index. 327 $aIll-Posed Problems in Imaging and Computer Vision -- Selection of the Regularization Parameter -- Introduction to Optimization -- Unconstrained Optimization -- Constrained Optimization -- Frequency-Domain Implementation of Regularization -- Iterative Methods -- Regularized Image Interpolation Based on Data Fusion -- Enhancement of Compressed Video -- Volumetric Description of Three-Dimensional Objects for Object Recognition -- Regularized 3D Image Smoothing -- Multi-Modal Scene Reconstruction Using Genetic Algorithm-Based Optimization -- Appendix A: Matrix-Vector Representation for Signal Transformation -- Appendix B: Discrete Fourier Transform -- Appendix C: 3D Data Acquisition and Geometric Surface Reconstruction -- Appendix D: Mathematical Appendix -- Index. 330 $aThis book presents practical optimization techniques used in image processing and computer vision problems. Ill-posed problems are introduced and used as examples to show how each type of problem is related to typical image processing and computer vision problems. Unconstrained optimization gives the best solution based on numerical minimization of a single, scalar-valued objective function or cost function. Unconstrained optimization problems have been intensively studied, and many algorithms and tools have been developed to solve them. Most practical optimization problems, however, arise with a set of constraints. Typical examples of constraints include: (i) pre-specified pixel intensity range, (ii) smoothness or correlation with neighboring information, (iii) existence on a certain contour of lines or curves, and (iv) given statistical or spectral characteristics of the solution. Regularized optimization is a special method used to solve a class of constrained optimization problems. The term regularization refers to the transformation of an objective function with constraints into a different objective function, automatically reflecting constraints in the unconstrained minimization process. Because of its simplicity and efficiency, regularized optimization has many application areas, such as image restoration, image reconstruction, optical flow estimation, etc. Optimization plays a major role in a wide variety of theories for image processing and computer vision. Various optimization techniques are used at different levels for these problems, and this volume summarizes and explains these techniques as applied to image processing and computer vision. 410 0$aAdvances in Computer Vision and Pattern Recognition,$x2191-6586 606 $aOptical data processing 606 $aSignal processing 606 $aImage processing 606 $aSpeech processing systems 606 $aAlgorithms 606 $aComputer science?Mathematics 606 $aComputer mathematics 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 606 $aMathematical Applications in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/M13110 615 0$aOptical data processing. 615 0$aSignal processing. 615 0$aImage processing. 615 0$aSpeech processing systems. 615 0$aAlgorithms. 615 0$aComputer science?Mathematics. 615 0$aComputer mathematics. 615 14$aImage Processing and Computer Vision. 615 24$aSignal, Image and Speech Processing. 615 24$aAlgorithm Analysis and Problem Complexity. 615 24$aMathematical Applications in Computer Science. 676 $a006.6 676 $a006.37 700 $aAbidi$b Mongi A$4aut$4http://id.loc.gov/vocabulary/relators/aut$0941428 702 $aGribok$b Andrei V$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aPaik$b Joonki$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910255017803321 996 $aOptimization Techniques in Computer Vision$92123466 997 $aUNINA