Saliency-based boundary object detection in naturally complex scenes

Abstract

A stochastic scheme is presented for cooperative detection of landmark objects distributed in roadway boundaries. By indexing chromatic diversity within a locally Gaussian color space, saliency patterns are extracted with respect to the as-is primary system. Through saccadic scan of the saliency patterns, boundary objects are successively articulated into a system of fractal attractors consistent with the ground-object structure. As the result, the fractal model is indicated within the perspective of the naturally complex scenes.