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lecnotes21 - 13.472J/1.128J/2.158J/16.940J COMPUTATIONAL...

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Unformatted text preview: 13.472J/1.128J/2.158J/16.940J COMPUTATIONAL GEOMETRY Lecture 21 Dr. K. H. Ko Prof. N. M. Patrikalakis Copyright c 2003 Massachusetts Institute of Technology Contents 21 Object Matching 2 21.1 Various matching methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 21.2 Methods through localization/registration . . . . . . . . . . . . . . . . . . . . 2 21.3 Classification of matching methods . . . . . . . . . . . . . . . . . . . . . . . . 2 21.4 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 21.4.1 Distance metric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 21.4.2 Distance between a point and a parametric surface . . . . . . . . . . . 4 21.4.3 Distance metric function . . . . . . . . . . . . . . . . . . . . . . . . . . 4 21.5 Matching problems : CGWOS, CPWOS, CGWS or CPWS . . . . . . . . . . . 5 21.5.1 Resolving the scaling effects . . . . . . . . . . . . . . . . . . . . . . . . 5 21.5.2 Rotation and translation . . . . . . . . . . . . . . . . . . . . . . . . . . 5 21.6 Matching problems : IGWOS, IPWOS, IGWS or IPWS . . . . . . . . . . . . . 6 21.6.1 Iterative Closest Point (ICP) algorithm [1] for IGWOS or IPWOS . . . 6 21.6.2 ICP algorithm for scaling effects . . . . . . . . . . . . . . . . . . . . . . 6 21.7 Matching problems : NGWOS or NPWOS . . . . . . . . . . . . . . . . . . . . 7 21.7.1 Search method [2] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 21.7.2 KH method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 21.8 Matching problems : NGWS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 21.9 Matching problems : NPWS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 21.9.1 Umbilical point method [7] . . . . . . . . . . . . . . . . . . . . . . . . . 9 21.9.2 Optimization method [7] . . . . . . . . . . . . . . . . . . . . . . . . . . 12 21.10Matching problems : offset method . . . . . . . . . . . . . . . . . . . . . . . . 13 21.10.1Distance function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 21.10.2Objective function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 21.10.3Gradient vector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Bibliography 16 1 Lecture 21 Object Matching 21.1 Various matching methods Moment method Principal component Contour and silhouette New representation Localization/registration Miscellaneous approaches 21.2 Methods through localization/registration A basic goal of matching through localization/registration is to find the best rigid body trans- formation which aligns two objects as closely as possible. The correspondence search between two objects is a key issue in finding the best transformation for matching. Correspondence can be established by calculating distinct features of one object and locating the same ones on the other object. Therefore, the features have to be carefully chosen such that they are robustly extracted and invariant with respect to various transformations. Among various fea-robustly extracted and invariant with respect to various transformations....
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This note was uploaded on 11/08/2011 for the course AERO 16.872 taught by Professor Danielhastings during the Fall '03 term at MIT.

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lecnotes21 - 13.472J/1.128J/2.158J/16.940J COMPUTATIONAL...

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