: A new method is presented to learn object categories from unlabeled and unsegmented images for generic object recognition. We assume that each object can be characterized by a se...
Andreas Opelt, Axel Pinz, Michael Fussenegger, Pet...
Various local descriptors have been used successfully in a variety of tasks including object recognition. Although different descriptors have been shown to have different strength...
We introduce an object recognition system in which objects are represented as a sparse and spatially organized set of local (bent) line segments. The line segments correspond to b...
This contribution proposes a compositionality architecture for visual object categorization, i.e., learning and recognizing multiple visual object classes in unsegmented, cluttered...
In this paper we explore the interlink between temporally dense view-based object recognition and sparse image representations with local keypoints. The temporal component is an a...