We present a "parts and structure" model for object category recognition that can be learnt efficiently and in a semisupervised manner: the model is learnt from example ...
In this paper, we present a learning procedure called probabilistic boosting network (PBN) for joint real-time object detection and pose estimation. Grounded on the law of total p...
Jingdan Zhang, Shaohua Kevin Zhou, Leonard McMilla...
In this paper, we introduce a novel real-time tracker based on color, texture and motion information. RGB color histogram and correlogram (autocorrelogram) are exploited as color ...
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 this paper, a new approach for object detection and pose estimation is introduced. The contribution consists in the conception of entities permitting stable detection and relia...
Stefan Hinterstoisser, Selim Benhimane, Nassir Nav...