9783843376426 - Labeling Problems with Smoothness-Based Priors in Computer Vision: Formulations, Optimizations and Applications de CHEN, Shifeng (8 resultados)

Condición: New. pp. 156.

Taschenbuch. Condición: Neu. Labeling Problems with Smoothness-Based Priors in Computer Vision | Formulations, Optimizations and Applications | Shifeng Chen | Taschenbuch | 156 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783843376426 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Many applications in computer vision can be formulated as labeling problems of assigning each pixel a label where the labels represent some local quantities. To improve results of these labeling problems, smoothness-based priors can be enforced in the formulations.Such labeling problems with smoothness-based priors can be solved by minimizing a Markov energy. According to different definitions of the energy functions, different optimization tools can be used to obtain the results. In this book, three optimization approaches are used due to their good performance: graph cuts, belief propagation, and optimization with a closed form solution. Five algorithms in different applications are proposed in this book. All of them are formulated as smoothness based labeling problems, including single image segmentation, video object cutout, image/video completion, image denoising, and image matting. This book should be especially useful to professionals in computer vision fields. 156 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: CHEN ShifengShifeng Chen, PhD: Studied Information Engineering at The Chinese University of Hong Kong, China. Research assistant professor at Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, China.Many a.

Condición: New. Print on Demand pp. 156 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Condición: New. PRINT ON DEMAND pp. 156.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Many applications in computer vision can be formulated as labeling problems of assigning each pixel a label where the labels represent some local quantities. To improve results of these labeling problems, smoothness-based priors can be enforced in the formulations.Such labeling problems with smoothness-based priors can be solved by minimizing a Markov energy. According to different definitions of the energy functions, different optimization tools can be used to obtain the results. In this book, three optimization approaches are used due to their good performance: graph cuts, belief propagation, and optimization with a closed form solution. Five algorithms in different applications are proposed in this book. All of them are formulated as smoothness based labeling problems, including single image segmentation, video object cutout, image/video completion, image denoising, and image matting. This book should be especially useful to professionals in computer vision fields.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 156 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Many applications in computer vision can be formulated as labeling problems of assigning each pixel a label where the labels represent some local quantities. To improve results of these labeling problems, smoothness-based priors can be enforced in the formulations.Such labeling problems with smoothness-based priors can be solved by minimizing a Markov energy. According to different definitions of the energy functions, different optimization tools can be used to obtain the results. In this book, three optimization approaches are used due to their good performance: graph cuts, belief propagation, and optimization with a closed form solution. Five algorithms in different applications are proposed in this book. All of them are formulated as smoothness based labeling problems, including single image segmentation, video object cutout, image/video completion, image denoising, and image matting. This book should be especially useful to professionals in computer vision fields.