000			02083cam a2200277zu 4500
001			88965955
003			FRCYB88965955
005			20250429184238.0
006			m o d
007			cr un
008			250429s2023 fr | o|||||0|0|||eng d
020			_a9780128053201
035			_aFRCYB88965955
040			_aFR-PaCSA _ben _c _erda
100	1		_aBen Ayed, Ismail
245	0	1	_aHigh-Order Models in Semantic Image Segmentation _c['Ben Ayed, Ismail']
264		1	_bAcademic Press _c2023
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aBen Ayed, Ismail
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88965955 _qtext/html _a
520			_aHigh-Order Models in Semantic Image Segmentation reviews recent developments in optimization-based methods for image segmentation, presenting several geometric and mathematical models that underlie a broad class of recent segmentation techniques. Focusing on impactful algorithms in the computer vision community in the last 10 years, the book includes sections on graph-theoretic and continuous relaxation techniques, which can compute globally optimal solutions for many problems. The book provides a practical and accessible introduction to these state-of -the-art segmentation techniques that is ideal for academics, industry researchers, and graduate students in computer vision, machine learning and medical imaging. - Gives an intuitive and conceptual understanding of this mathematically involved subject by using a large number of graphical illustrations - Provides the right amount of knowledge to apply sophisticated techniques for a wide range of new applications - Contains numerous tables that compare different algorithms, facilitating the appropriate choice of algorithm for the intended application - Presents an array of practical applications in computer vision and medical imaging - Includes code for many of the algorithms that is available on the book's companion website
999			_c1331285 _d1331285