Title :
Noise resistant projective and affine invariants
Author_Institution :
Center for Autom. Res., Maryland Univ., College Park, MD, USA
Abstract :
A method of obtaining local projective and affine invariants that is more robust than existing methods is presented. These shape descriptors are useful for object recognition because they eliminate the search for the unknown viewpoint. Being local, these invariants are much less sensitive to occlusion than the global ones used elsewhere. The basic ideas are (i) using an implicit curve representation without a curve parameter, thus increasing robustness; and (ii) using a canonical coordinate system which is defined by the intrinsic properties of the shape, regardless of any given coordinate system, and is thus invariant. Several configurations are treated: a general curve without any correspondence, and curves with known correspondence of feature points or lines
Keywords :
computational geometry; image processing; image recognition; invariance; affine invariants; canonical coordinate system; feature points; implicit curve representation; noise resistant projective invariants; object recognition; occlusion; shape descriptors; Automation; Educational institutions; Face recognition; Libraries; Noise robustness; Noise shaping; Object recognition; Polynomials; Shape;
Conference_Titel :
Computer Vision and Pattern Recognition, 1992. Proceedings CVPR '92., 1992 IEEE Computer Society Conference on
Conference_Location :
Champaign, IL
Print_ISBN :
0-8186-2855-3
DOI :
10.1109/CVPR.1992.223218