JOURNAL ARTICLE

Matching of 3-D curves using semi-differential invariants

Abstract

A method for matching 3-D curves under Euclidean motions is presented. Our approach uses a semi-differential invariant description requiring only first derivatives and one reference point, thus avoiding the computation of high order derivatives. A novel curve similarity measure building on the notion of /spl epsiv/-reciprocal correspondence is proposed. It is shown that by combining /spl epsiv/-reciprocal correspondence with the robust least median of squares motion estimation, the registration of partially occluded curves can be accomplished. An experiment with real curves extracted from 3-D surfaces demonstrates that curve matching can be successfully performed even on data from a simple and cheap 3-D sensor.< >

Keywords:
Similarity (geometry) Reciprocal Invariant (physics) Matching (statistics) Mathematics Computation Curve fitting Artificial intelligence Euclidean geometry Similarity measure Algorithm Computer science Geometry Image (mathematics)

Metrics

113
Cited By
5.90
FWCI (Field Weighted Citation Impact)
9
Refs
0.97
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Vision and Imaging
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Image and Object Detection Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Robotics and Sensor-Based Localization
Physical Sciences →  Engineering →  Aerospace Engineering

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