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008			220601s2016 sz | s |||| 0|eng d
020			_a9783031018152
024	7		_a10.1007/978-3-031-01815-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA297.6 _b2016 EB
100	1		_aKanatani, Kenʼichi, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9101824 _d1947-
245	1	0	_aEllipse Fitting for Computer Vision : _bImplementation and Applications _cby Kenichi Kanatani, Yasuyuki Sugaya, Yasushi Kanazawa
250			_a1st edition 2016
264		1	_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XII, 128 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Computer Vision _x2153-1064
505	0		_aPreface -- Introduction -- Algebraic Fitting -- Geometric Fitting -- Robust Fitting -- Ellipse-based 3-D Computation -- Experiments and Examples -- Extension and Generalization -- Accuracy of Algebraic Fitting -- Maximum Likelihood and Geometric Fitting -- Theoretical Accuracy Limit -- Answers -- Bibliography -- Authors' Biographies -- Index .
520			_aBecause circular objects are projected to ellipses in images, ellipse fitting is a first step for 3-D analysis of circular objects in computer vision applications. For this reason, the study of ellipse fitting began as soon as computers came into use for image analysis in the 1970s, but it is only recently that optimal computation techniques based on the statistical properties of noise were established. These include renormalization (1993), which was then improved as FNS (2000) and HEIV (2000). Later, further improvements, called hyperaccurate correction (2006), HyperLS (2009), and hyper-renormalization (2012), were presented. Today, these are regarded as the most accurate fitting methods among all known techniques. This book describes these algorithms as well implementation details and applications to 3-D scene analysis. We also present general mathematical theories of statistical optimization underlying all ellipse fitting algorithms, including rigorous covariance and bias analyses and the theoretical accuracy limit. The results can be directly applied to other computer vision tasks including computing fundamental matrices and homographies between images. This book can serve not simply as a reference of ellipse fitting algorithms for researchers, but also as learning material for beginners who want to start computer vision research. The sample program codes are downloadable from the website: https://sites.google.com/a/morganclaypool.com/ellipse-fitting-for-computer-vision-implementation-and-applications.
988			_aSynthesis Collection of Technology_2016
650		7	_2embne _9159793 _aVisión por ordenador _xModelos matemáticos
700			_aSugaya, Yasuyuki _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9101825
700	1		_aKanazawa, Yasushi _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9101826
776	0	8	_iPrinted edition: _z9783031000768
776	0	8	_iPrinted edition: _z9783031006876
776	0	8	_iPrinted edition: _z9783031029431
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01815-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI