Efficient Invariant Representations
International Journal of Computer Vision
Perceptual organization with image formation compatibilities
Pattern Recognition Letters
Camera Calibration from Surfaces of Revolution
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovery of Circular Motion from Profiles of Surfaces
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Finding Objects by Grouping Primitives
Shape, Contour and Grouping in Computer Vision
Geometric Grouping of Repeated Elements within Images
Shape, Contour and Grouping in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In any object recognition system a major and primary task is to associate those image features, within an image of a complex scene, that arise from an individual object. The key idea here is that a geometric class defined in 3D induces relationships in the image which must hold between points on the image outline (the perspective projection of the object). The resulting image constraints enable both identification and grouping of image features belonging to objects of that class. The classes include surfaces of revolution, canal surfaces (pipes) and polyhedra. Recognition proceeds by first recognising an object as belonging to one of the classes (for example a surface of revolution) and subsequently identifying the object (for example as a particular vase). This differs from conventional object recognition systems where recognition is generally targetted at particular objects. These classes also support the computation of 3D invariant descriptions including symmetry axes, canonical coordinate frames and projective signatures. The constraints and grouping methods are viewpoint invariant, and proceed with no information on object pose. We demonstrate the effectiveness of this class-based grouping on real, cluttered scenes using grouping algorithms developed for rotationally symmetric surfaces, canal-surfaces and polyhedra.