Abstract
A machine vision algorithm to find the longest common subcurve of two 3-D curves is presented. The curves are represented by splines fitted through sequences of sample points extracted from dense range data. The approximated 3-D curves are transformed into 1-D numerical strings of rotation and translation invariant shape signatures, based on a multi-resolution representation of the curvature and torsion values of the space curves. The shape signature strings are matched using an efficient hashing technique that finds longest matching substrings. The results of the string matching stage are later verified by a robust, least-squares, 3-D curve matching technique, which also recovers the Euclidean transformation between the curves being matched. This algorithm is of average complexity O(n) where n is the number of the sample points on the two curves. The algorithm has applications in assembly and object recognition tasks. Results of assembly experiments are included.
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References
E. Kishon and H.J. Wolfson. 3-D Curve Matching. In Proceedings of the AAAI workshop on Spatial Reasoning and Multi-Sensor Fusion, pages 250–261, St. Charles, Ill., 1987.
J. T. Schwartz and M. Sharir. Identification ot Partially Obscured Objects in Two or Three Dimensions by Matching of Noisy Characteristic Curves. The International Journal of Robotics Research, 6(2):29–44, 1987.
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© 1990 Springer-Verlag Berlin Heidelberg
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Kishon, E., Hastie, T., Wolfson, H. (1990). 3-D curve matching using splines. In: Faugeras, O. (eds) Computer Vision — ECCV 90. ECCV 1990. Lecture Notes in Computer Science, vol 427. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0014915
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DOI: https://doi.org/10.1007/BFb0014915
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