We propose a new feature detector based on the Weighted Alpha Shapes. The detected features are blob-like and include non-extremal regions as well as regions determined by cavities of boundary shape.

Medial features are image regions of arbitrary scale and shape, extracted without explicit scale space construction. They rely on a weighted distance map of image gradient, computed using an exact linear-time algorithm. The corresponding weighted medial axis is then decomposed into a graph representing image structure. A duality property enables reconstruction of regions using the same distance propagation. We select features according to our shape fragmentation factor, favoring those well enclosed by boundaries.

We propose a detector that starts from single scale edges and produces reliable and interpretable blob-like regions and groups of regions of arbitrary shape. The detector is based on merging local maxima of the binary distance transform guided by the gradient strength of the surrounding edges.

We use saliency for spatiotemporal feature detection in videos by incorporating color and motion apart from intensity. Saliency is computed by a global minimization process constrained by pure volumetric constraints, each of them being related to an informative visual aspect inspired by the Gestalt theory.

Based on established computational models of visual attention we propose novel models and methods both for spatial (images) and spatiotemporal (video sequences) analysis. Applications include visual classification and spatiotemporal feature detection.