Showing papers by "Shihui Ying published in 2007"
12 Nov 2007
TL;DR: This paper introduces a novel approach named the scaling iterative closest point (SICP) algorithm which integrates a scale matrix with boundaries into the original ICP algorithm for scaling registration.
Abstract: The ICP algorithm is accurate and fast for registration between two point sets in a same scale, but it doesn't handle the case with different scales. This paper instead introduces a novel approach named the scaling iterative closest point (SICP) algorithm which integrates a scale matrix with boundaries into the original ICP algorithm for scaling registration. This method uses a simple iterative algorithm with the SVD algorithm and the properties of parabola incorporated to compute the translation, rotation and scale transformations at each iterative step, and its convergence is rapid with only a few iterations. The SICP algorithm is independent of shape representation and feature extraction; thereby it is general for scaling registration. Experimental results demonstrate its robustness and fast speed compared with the standard ICP algorithm.
66 citations
02 Jul 2007
TL;DR: A novel approach named the iterative closest point with bounded scale (ICPBS) algorithm which integrates a scale with boundaries into the traditional ICP algorithm, and yields more satisfying robust results than thetraditional ICP method.
Abstract: The iterative closest point (ICP) algorithm is an accurate and fast approach for registration between two point sets in a same scale, but it doesn't handle the case with different scales. This paper instead introduces a novel approach named the iterative closest point with bounded scale (ICPBS) algorithm which integrates a scale with boundaries into the traditional ICP algorithm. This proposed technique uses the singular value decomposition algorithm and the properties of parabola to compute the similar transformation at each iterative step, and yields more satisfying robust results than the traditional ICP method in registration between two m-D point sets with different scales. Experimental results demonstrate the presented method is robust and fast for practical use.
27 citations
01 Jan 2007
TL;DR: A novel algorithm is introduced named the Iterative Closest Point withBoundedScale (ICPBS) algorithm whichintegrates ascale withboundaries into the traditional ICPalgorithm and yields moresatisfying robust results.
Abstract: TheIterative Closest Point (ICP) algorithm isanaccurate andfast approach forregistration between twopoint sets in asamescale, butitdoesn't handle thecasewithdifferent scales. Thispaperinstead introduces a novelapproach namedtheIterative Closest PointwithBoundedScale (ICPBS) algorithm whichintegrates ascale withboundaries into thetraditional ICPalgorithm. Thisproposed technique usesthesingular valuedecomposition algorithm andthe properties ofparabola tocompute thesimilar transformation ateachiterative step, andyields moresatisfying robust results thanthetraditional ICPmethodinregistration betweentwo m-D pointsetswithdifferent scales. Experimental results demonstrate thepresented methodis robust andfast forpractical use.