@article{95a0534ac4f741c5bf8f38c00eef4ae4,
title = "On Curve Matching",
abstract = "Two algorithms to find the longest common subcurve of two 2-D curves are presented. These algorithms are based on conversion of the curves into shape signature strings and application of string matching techniques to find long matching substrings. Then direct curve matching is applied to the corresponding {\textquoteleft}candidate{\textquoteright} subcurves to find the longest matching subcurve. The first algorithm is of complexity O(n), where n is the number of sample points on the curves. The second one, while being theoretically somewhat less efficient, proved to be robust and efficient in practical applications. Both algorithms solve the problem for general curves without being dependent on some set of special points on the curves. The algorithms have industrial applications to problems of object assembly and object recognition. Experimental results are included. The algorithms can be easily extended to the 3-D case.",
keywords = "Computer vision, curvature, curve matching, machine intelligence, object recognition, part assembly, pattern recognition, string matching",
author = "Wolfson, {Haim J.}",
note = "Funding Information: The problem of finding the best fit between two curves is of central importance in robotic applications of computer vision. This problem appears in various assembly tasks requiring a robot to put two pieces together along their matching boundary, e.g., “puzzle assembly” [ I]-[3]. Another major application for such an algorithm is in recognition and location of partially occluded objects in an overlapping scene [4]-[6]. Since two-dimensional objects are completely described, both globally and locally, by their closed boundary curves, the detection of partially occluded objects participating in a composite scene can be done by matching the boundary curve of the scene with the boundary curves of candidate objects, and trying to find out whether they have a “long enough” matching subcurve. This method is particularly attractive in recognition of partially occluded objects, because in such a situation no use can come of global object characteristics, and only properties which are preserved locally, such as the visible boundary curve, can be taken into account. The method can also be applied to three-dimensional objects which are flat enough, or have a small number of stable positions, so that the orthogonal projection of each such position can be taken as a 2-D model. Curve matching algorithms also have potential applications in finding correspondence between maps and terrain images. An earlier version of AZgorithrn 11 of this Manuscript received July 29, 1988; revised September 11. 1989. This work was supported by Office of Naval Research under Grant N00014-82-K-038 I, the National Science Foundation under Grant NSF-DCR-83-20085, and by grants from Digital Equipment Corporation and IBM Corporation.",
year = "1990",
month = may,
doi = "10.1109/34.55108",
language = "אנגלית",
volume = "12",
pages = "483--489",
journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",
issn = "0162-8828",
publisher = "IEEE Computer Society",
number = "5",
}