000 04060nam a22005535i 4500
001 978-3-031-01815-2
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008 220601s2016 sz | s |||| 0|eng d
020 _a9783031018152
_9978-3-031-01815-2
024 7 _a10.1007/978-3-031-01815-2
_2doi
050 4 _aTA1501-1820
050 4 _aTA1634
072 7 _aUYT
_2bicssc
072 7 _aCOM016000
_2bisacsh
072 7 _aUYT
_2thema
082 0 4 _a006
_223
100 1 _aKanatani, Kenichi.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982139
245 1 0 _aEllipse Fitting for Computer Vision
_h[electronic resource] :
_bImplementation and Applications /
_cby Kenichi Kanatani, Yasuyuki Sugaya, Yasushi Kanazawa.
250 _a1st ed. 2016.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXII, 128 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aImage processing
_xDigital techniques.
_94145
650 0 _aComputer vision.
_982140
650 0 _aPattern recognition systems.
_93953
650 1 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
_931569
650 2 4 _aComputer Vision.
_982141
650 2 4 _aAutomated Pattern Recognition.
_931568
700 1 _aSugaya, Yasuyuki.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982142
700 1 _aKanazawa, Yasushi.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982143
710 2 _aSpringerLink (Online service)
_982144
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031000768
776 0 8 _iPrinted edition:
_z9783031006876
776 0 8 _iPrinted edition:
_z9783031029431
830 0 _aSynthesis Lectures on Computer Vision,
_x2153-1064
_982145
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01815-2
912 _aZDB-2-SXSC
942 _cEBK
999 _c85303
_d85303