Current feature-based object recognition methods use information derived from local image patches. For robustness, features are engineered for invariance to various transformation...
We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constel...
We present a probabilistic approach to shape matching which is invariant to rotation, translation and scaling. Shapes are represented by unlabeled point sets, so discontinuous bou...
We address two-dimensional shape-based classification, considering shapes described by arbitrary sets of unlabeled points, or landmarks. This is relevant in practice because, in m...
In this paper we present a method to recognize shapes by analyzing a polygonal approximation of their boundaries. The method is independent of the used approximation method since i...