|Subject Domain (in DDC)||Computer science, information & general works ♦ Data processing & computer science|
|Subject Keyword||Several Application ♦ Real Data ♦ Optimal Transformation ♦ Consistent Correspondence ♦ Point Set ♦ Shape Matching ♦ Computational Geometry ♦ Standard Method ♦ Efficient Use ♦ Example Application ♦ Camera Pose ♦ Convex Programming ♦ Polynomial-time Bound ♦ Several Algorithm ♦ Possible Subset ♦ Projective Transformation ♦ Multiview Reconstruction Problem|
|Description||We present a framework for computing optimal transformations, aligning one point set to another, in the presence of outliers. Example applications include shape matching and registration (using, for example, similarity, affine or projective transformations) as well as multiview reconstruction problems (triangulation, camera pose etc.). While standard methods like RANSAC essentially use heuristics to cope with outliers, we seek to find the largest possible subset of consistent correspondences and the globally optimal transformation aligning the point sets. Based on theory from computational geometry, we show that this is indeed possible to accomplish in polynomial-time. We develop several algorithms which make efficient use of convex programming. The scheme has been tested and evaluated on both synthetic and real data for several applications. 1 1.
|Educational Role||Student ♦ Teacher|
|Age Range||above 22 year|
|Education Level||UG and PG ♦ Career/Technical Study|
|Learning Resource Type||Article|
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