Title :
Differential invariants without derivatives
Author_Institution :
Center for Autom. Res., Maryland Univ., College Park, MD, USA
fDate :
30 Aug-3 Sep 1992
Abstract :
Presents a new and more robust method of obtaining local projective and affine invariants. These shape descriptors are useful for object recognition because they eliminate the search for the unknown viewpoint. Being local, the invariants are much less sensitive to occlusion than the global ones used elsewhere. The basic ideas are: (i) employing 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 including a general curve without any correspondence, and curves with known correspondence of feature points or lines
Keywords :
image recognition; polynomials; canonical coordinate system; differential invariants; feature points; image recognition; implicit curve representation; local projective; object recognition; polynomials; shape descriptors; Automation; Educational institutions; Face detection; Face recognition; Image reconstruction; Libraries; Object recognition; Polynomials; Robustness; Shape;
Conference_Titel :
Pattern Recognition, 1992. Vol.III. Conference C: Image, Speech and Signal Analysis, Proceedings., 11th IAPR International Conference on
Conference_Location :
The Hague
Print_ISBN :
0-8186-2920-7
DOI :
10.1109/ICPR.1992.202007