PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Fitting Superellipses to Incomplete Contours
Mihai Osian, Tinne Tuytelaars and Luc Van Gool
In: IEEE workshop on Perceptual Organization in Computer Vision, 28 June 2004, Washington, USA.

Abstract

Affine invariant regions have proved a powerful feature for object recognition and categorization. These features heavily rely on object textures rather than shapes, however. Typically, their shapes have been fixed to ellipses or parallelograms. The paper proposes a novel affine invariant region type, that is built up from a combination of fitted superellipses. These novel features have the advantage of offering a much wider range of shapes through the addition of a very limited number of shape parameters, with the traditional ellipses and parallelograms as subsets. The paper offers a solution for the robust fitting of superellipses to partial contours, which is a crucial step towards the implementation of the novel features.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:166
Deposited By:Tinne Tuytelaars
Deposited On:03 June 2004