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...
Sets of local features that are invariant to common image transformations are an effective representation to use when comparing images; current methods typically judge feature set...
There have been important recent advances in object recognition through the matching of invariant local image features. However, the existing approaches are based on matching to i...
In this paper, we consider the problem of categorizing
videos of dynamic textures under varying view-point. We
propose to model each video with a collection of Linear
Dynamics S...
Most existing approaches for learning action models work by extracting suitable low-level features and then training appropriate classifiers. Such approaches require large amount...