000			04196nam a22003975i 4500
999			_c88191 _d88191 _x1
001			88191
003			ES-MaUEC
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008			161206s2016 gw | s |||| 0|eng d
020			_a9783319463643
024	7		_a10.1007/978-3-319-46364-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA1634 _b2016 EB
100	1		_aAbidi, Mongi A. _9101716
245	1	0	_aOptimization Techniques in Computer Vision : _bIll-Posed Problems and Regularization _cby Mongi A. Abidi, Andrei V. Gribok, Joonki Paik
264		1	_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XV, 293 páginas) _b127 ilustraciones, 23 ilustraciones en color
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	1		_aAdvances in Computer Vision and Pattern Recognition _x2191-6586
505	0		_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.
520			_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.
988			_aEBOOK, EBSPRINGER
650		7	_aVisión por ordenador _2embne _9159793
700	1		_aGribok, Andrei V. _9101717 _0Local
700	1		_aPaik, Joonki. _9101718 _0Local
830		0	_aAdvances in Computer Vision and Pattern Recognition _x2191-6586 _0http://id.loc.gov/authorities/names/no2011103580 _9134066
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-46364-3zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
907			_a.b12981953 _b10-10-17 _c08-03-17
942			_2lcc _cLE
945			_g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11603793 _z06-04-17
998			_b06/2020 _dz _eu _feng _ggw _h0