We describe a hierarchical probabilistic model for the detection and recognition of objects in cluttered, natural scenes. The model is based on a set of parts which describe the e...
Erik B. Sudderth, Antonio B. Torralba, William T. ...
One of the bottlenecks of current recognition (and graph matching) systems is their assumption of one-to-one feature (node) correspondence. This assumption breaks down in the gener...
Ali Shokoufandeh, Yakov Keselman, M. Fatih Demirci...
This paper presents a novel discriminative learning method, called Manifold Discriminant Analysis (MDA), to solve the problem of image set classification. By modeling each image s...
A well-built dataset is a necessary starting point for advanced computer vision research. It plays a crucial role in evaluation and provides a continuous challenge to stateof-the-...
In this paper a new solution for the single sample problem in low resolution face recognition is proposed. The proposed solution uses an enhanced virtual pose generation method to...