Curvature-based signatures for object description and recognition

Elli Angelopoulou; James P. Williams; Lawrence B. Wolff

doi:10.1117/12.263322

20 January 1997 Curvature-based signatures for object description and recognition

Elli Angelopoulou, James P. Williams, Lawrence B. Wolff

Proceedings Volume 2909, Three-Dimensional Imaging and Laser-Based Systems for Metrology and Inspection II; (1997) https://doi.org/10.1117/12.263322
Event: Photonics East '96, 1996, Boston, MA, United States

Abstract

An invariant related to Gaussian curvature at an object point is developed based upon the covariance matrix of photometric values within a local neighborhood about the point. We employ three illumination conditions, two of which are completely unknown. We never need to explicitly know the surface normal at a point. The determinant of the covariance matrix of the intensity three-tuples in the local neighborhood of an object point is shown to be invariant with respect to rotation and translation. A way of combing these determinant to form a signature distribution is formulated that is rotation, translation, and scale invariant. This signature is shown to be invariant over large ranges of poses of the same objects, while being significantly different between distinctly shaped objects. A new object recognition methodology is proposed by compiling signatures for only a few viewpoints of a given object.

Citation Download Citation

Elli Angelopoulou, James P. Williams, and Lawrence B. Wolff "Curvature-based signatures for object description and recognition", Proc. SPIE 2909, Three-Dimensional Imaging and Laser-Based Systems for Metrology and Inspection II, (20 January 1997); https://doi.org/10.1117/12.263322

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