This paper presents a novel theory for learning generic prior models from a set of observed natural images based on a minimax entropy theory that the authors studied in modeling t...
Recently the entropy-similarity measure has been introduced for the registration of image pairs prior to subtraction in medical imaging e.g. digital subtraction angiography (DSA). ...
We propose efficiency of representation as a criterion for evaluating shape models, then apply this criterion to compare the boundary curve representation with the medial axis. We...
Appropriate feature selection is a very crucial issue in any machine learning framework, specially in Maximum Entropy (ME). In this paper, the selection of appropriate features for...
We present an active learning framework to simultaneously learn appearance and contextual models for scene understanding tasks (multi-class classification). Existing multi-class a...