TY - JOUR
T1 - Partial surface and volume matching in three dimensions
AU - Barequet, Gill
AU - Sharir, Micha
N1 - Funding Information:
Work on this paper by the authors has been supported by the G.I.F.—the German-Israeli Foundation for Scientific Research and Development. Work by the first author has been supported also by the Israeli Ministry of Science and the Arts under an Eshkol Grant 0562-1-94. Work by the second author has been supported also by National Science Foundation Grants CCR-91-22103 and CCR-93-11127, by grants from the U.S.-Israeli Binational Science Foundation, and by the Fund for Basic Research administered by the Israeli Academy of Sciences.
PY - 1997
Y1 - 1997
N2 - In this paper we present a new technique for partial surface and volume matching of images in three dimensions. In this problem we are given two objects in 3-space, each represented as a set of points, and the goal is to find a rigid motion of one object which makes a sufficiently large portion of its boundary lying sufficiently close to a corresponding portion of the boundary of the second object. This is an important problem in pattern recognition and in computer vision, with many industrial, medical, and chemical applications. Our method treats separately the rotation and the translation components of the Euclidean motion that we seek. The algorithm steps through a sequence of rotations, in a steepest-descent style, and uses a novel technique for scoring the match for any fixed rotation. Experimental results on various examples, involving data from industrial applications, medical imaging, and molecular biology, are presented, and show the accurate and robust performance of our algorithm.
AB - In this paper we present a new technique for partial surface and volume matching of images in three dimensions. In this problem we are given two objects in 3-space, each represented as a set of points, and the goal is to find a rigid motion of one object which makes a sufficiently large portion of its boundary lying sufficiently close to a corresponding portion of the boundary of the second object. This is an important problem in pattern recognition and in computer vision, with many industrial, medical, and chemical applications. Our method treats separately the rotation and the translation components of the Euclidean motion that we seek. The algorithm steps through a sequence of rotations, in a steepest-descent style, and uses a novel technique for scoring the match for any fixed rotation. Experimental results on various examples, involving data from industrial applications, medical imaging, and molecular biology, are presented, and show the accurate and robust performance of our algorithm.
KW - Computer vision
KW - Geometric hashing
KW - Molecule docking
KW - Partial surface matching
KW - Pattern recognition
KW - Protein matching
UR - http://www.scopus.com/inward/record.url?scp=0031233941&partnerID=8YFLogxK
U2 - 10.1109/34.615444
DO - 10.1109/34.615444
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AN - SCOPUS:0031233941
SN - 0162-8828
VL - 19
SP - 929
EP - 948
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
JF - IEEE Transactions on Pattern Analysis and Machine Intelligence
IS - 9
ER -