000 03241nam a22005295i 4500
001 978-3-031-01818-3
003 DE-He213
005 20240730163724.0
007 cr nn 008mamaa
008 220601s2017 sz | s |||| 0|eng d
020 _a9783031018183
_9978-3-031-01818-3
024 7 _a10.1007/978-3-031-01818-3
_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 _aChin, Tat-Jun.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980185
245 1 4 _aThe Maximum Consensus Problem
_h[electronic resource] :
_bRecent Algorithmic Advances /
_cby Tat-Jun Chin, David Suter.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXV, 178 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 -- Acknowledgments -- The Maximum Consensus Problem -- Approximate Algorithms -- Exact Algorithms -- Preprocessing for Maximum Consensus -- Appendix -- Bibliography -- Authors' Biographies -- Index .
520 _aOutlier-contaminated data is a fact of life in computer vision. For computer vision applications to perform reliably and accurately in practical settings, the processing of the input data must be conducted in a robust manner. In this context, the maximum consensus robust criterion plays a critical role by allowing the quantity of interest to be estimated from noisy and outlier-prone visual measurements. The maximum consensus problem refers to the problem of optimizing the quantity of interest according to the maximum consensus criterion. This book provides an overview of the algorithms for performing this optimization. The emphasis is on the basic operation or "inner workings" of the algorithms, and on their mathematical characteristics in terms of optimality and efficiency. The applicability of the techniques to common computer vision tasks is also highlighted. By collecting existing techniques in a single article, this book aims to trigger further developments in this theoretically interesting and practically important area.
650 0 _aImage processing
_xDigital techniques.
_94145
650 0 _aComputer vision.
_980186
650 0 _aPattern recognition systems.
_93953
650 1 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
_931569
650 2 4 _aComputer Vision.
_980187
650 2 4 _aAutomated Pattern Recognition.
_931568
700 1 _aSuter, David.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980188
710 2 _aSpringerLink (Online service)
_980189
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031006906
776 0 8 _iPrinted edition:
_z9783031029462
830 0 _aSynthesis Lectures on Computer Vision,
_x2153-1064
_980190
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01818-3
912 _aZDB-2-SXSC
942 _cEBK
999 _c84914
_d84914