| 000 | 05578nam a2200637 i 4500 | ||
|---|---|---|---|
| 001 | 7873535 | ||
| 003 | IEEE | ||
| 005 | 20200413152923.0 | ||
| 006 | m eo d | ||
| 007 | cr cn |||m|||a | ||
| 008 | 170321s2017 caua foab 001 0 eng d | ||
| 020 |
_a9781627052863 _qebook |
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| 020 |
_z9781627052924 _qprint |
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| 024 | 7 |
_a10.2200/S00757ED1V01Y201702COV011 _2doi |
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| 035 | _a(CaBNVSL)swl00407234 | ||
| 035 | _a(OCoLC)978253071 | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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| 050 | 4 |
_aTA1634 _b.C455 2017 |
|
| 082 | 0 | 4 |
_a006.37 _223 |
| 100 | 1 |
_aChin, Tat-Jun, _eauthor. |
|
| 245 | 1 | 4 |
_aThe maximum consensus problem : _brecent algorithmic advances / _cTat-Jun Chin and David Suter. |
| 264 | 1 |
_a[San Rafael, California] : _bMorgan & Claypool, _c2017. |
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| 300 |
_a1 PDF (xiii, 178 pages) : _billustrations. |
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| 336 |
_atext _2rdacontent |
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| 337 |
_aelectronic _2isbdmedia |
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| 338 |
_aonline resource _2rdacarrier |
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| 490 | 1 |
_aSynthesis lectures on computer vision, _x2153-1064 ; _v# 11 |
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| 538 | _aMode of access: World Wide Web. | ||
| 538 | _aSystem requirements: Adobe Acrobat Reader. | ||
| 500 | _aPart of: Synthesis digital library of engineering and computer science. | ||
| 504 | _aIncludes bibliographical references (pages 163-173) and index. | ||
| 505 | 8 | _aAppendix -- Bibliography -- Authors' biographies -- Index. | |
| 505 | 8 | _a4. Preprocessing for maximum consensus -- 4.1 Introduction -- 4.1.1 Guaranteed outlier removal -- 4.2 Geometrically inspired approaches -- 4.2.1 2D rigid transformation -- 4.2.2 3D rotational alignment -- 4.3 Integer linear programming approach -- 4.3.1 An integer linear program formulation for GORE -- 4.3.2 Generalised fractional models -- 4.4 Bibliographical remarks -- | |
| 505 | 8 | _a3. Exact algorithms -- 3.1 Introduction -- 3.2 Optimal line fitting -- 3.2.1 Characterization of the solution -- 3.2.2 Plane sweep method -- 3.3 Integer linear programming method -- 3.3.1 Numerical accuracy and performance -- 3.3.2 Generalized fractional models -- 3.4 Robust point set registration -- 3.4.1 Rotational alignment -- 3.4.2 Euclidean registration -- 3.5 Tractable algorithms with subset search -- 3.5.1 Characterization of the solution -- 3.5.2 Subset enumeration -- 3.6 Tree search -- 3.6.1 Existence of tree structure -- 3.6.2 Breadth first search -- 3.6.3 A* search -- 3.7 Bibliographical remarks -- | |
| 505 | 8 | _a2. Approximate algorithms -- 2.1 Introduction -- 2.2 Random sample consensus -- 2.2.1 Extensions and improvements -- 2.2.2 Data span and quasidegeneracy -- 2.3 L1 minimization -- 2.3.1 Generalized fractional models -- 2.4 Chebyshev approximation -- 2.4.1 Characterization of the Chebyshev estimate -- 2.4.2 Outlier removal with L[infinity] minimization -- 2.4.3 Generalised fractional programming -- 2.5 LP-type problems -- 2.5.1 Definition and properties -- 2.5.2 Solving LP-type problems -- 2.5.3 Outlier removal for LP-type problems -- 2.6 The K-slack method -- 2.6.1 A relaxed minimax formulation -- 2.6.2 Outlier removal with the K-slack method -- 2.7 Exact penalty method -- 2.7.1 Penalized formulation -- 2.7.2 Deterministic local refinement algorithm -- 2.8 Evaluation -- 2.9 Bibliographical remarks -- | |
| 505 | 0 | _a1. The maximum consensus problem -- 1.1 Introduction -- 1.1.1 Problem definition -- 1.1.2 What is this book about? -- 1.1.3 Road map -- 1.2 Relation to other robust fitting methods -- 1.2.1 Hough transform -- 1.2.2 M-estimator -- 1.2.3 Least median squares -- 1.3 Problem difficulty -- 1.3.1 Exact vs. approximate solutions -- 1.3.2 Computational hardness -- 1.4 Bibliographical remarks -- | |
| 506 | _aAbstract freely available; full-text restricted to subscribers or individual document purchasers. | ||
| 510 | 0 | _aGoogle scholar | |
| 510 | 0 | _aGoogle book search | |
| 510 | 0 | _aINSPEC | |
| 510 | 0 | _aCompendex | |
| 520 | 3 | _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. | |
| 530 | _aAlso available in print. | ||
| 588 | _aTitle from PDF title page (viewed on March 21, 2017). | ||
| 650 | 0 | _aRobust optimization. | |
| 650 | 0 |
_aComputer vision _xMathematical models. |
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| 653 | _aoptimization | ||
| 653 | _aalgorithms | ||
| 653 | _amaximum consensus | ||
| 653 | _arobust fitting | ||
| 700 | 1 |
_aSuter, David, _eauthor. |
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| 776 | 0 | 8 |
_iPrint version: _z9781627052924 |
| 830 | 0 |
_aSynthesis lectures on computer vision ; _v# 11. _x2153-1064 |
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| 830 | 0 | _aSynthesis digital library of engineering and computer science. | |
| 856 | 4 | 2 |
_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=7873535 |
| 999 |
_c562250 _d562250 |
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