In this paper, we propose a general framework for fusing bottom-up segmentation with top-down object behavior classification over an image sequence. This approach is beneficial fo...
In this work we introduce a novel approach to object categorization that incorporates two types of context ? cooccurrence and relative location ? with local appearancebased featur...
Carolina Galleguillos, Andrew Rabinovich, Serge Be...
In contrast to traditional Markov random field (MRF) models, we develop a Steerable Random Field (SRF) in which the field potentials are defined in terms of filter responses that ...
In this paper, we propose a component-based discriminative approach for face alignment without requiring initialization1 . Unlike many approaches which locally optimize in a small ...
In this paper, we present a framework for the design of steganographic schemes that can provide provable security by achieving zero Kullback-Leibler divergence between the cover a...