On the Role of Contrast and Regularity in Perceptual Boundary Saliency
This work addresses shape recognition and analysis in computer vision, offering incremental improvements to existing methods.
The paper tackles the problem of detecting perceptually significant boundaries in images by allowing partially salient level lines, resulting in more robust and stable detections, and shows good performance in natural images.
Mathematical Morphology proposes to extract shapes from images as connected components of level sets. These methods prove very suitable for shape recognition and analysis. We present a method to select the perceptually significant (i.e., contrasted) level lines (boundaries of level sets), using the Helmholtz principle as first proposed by Desolneux et al. Contrarily to the classical formulation by Desolneux et al. where level lines must be entirely salient, the proposed method allows to detect partially salient level lines, thus resulting in more robust and more stable detections. We then tackle the problem of combining two gestalts as a measure of saliency and propose a method that reinforces detections. Results in natural images show the good performance of the proposed methods.