A contrario edge detection with edgelets

Edge detection remains an active problem in the image processing community, because of the high complexity of natural images. In the last decade, Desolneux et al. proposed a novel parameter free detection approach, based on the Helmhotz principle. Applied to the edge detection problem, this means that observing a true edge in random and independent conditions is very unlikely, thus, such events are considered meaningful. However, overdetection may occur, partly due to the use of a single pixel-wise feature. In this paper, we propose to introduce higher level information in the a contrario framework, by computing several features along a set of connected pixels (an edgelet). Among the features, we introduce a shape prior, learned on a database. We propose to estimate the a contrario distributions of the two other features, namely the gradient and the texture, by a Monte-Carlo simulation approach. Experiments show that our method improves the original one, by decreasing the number of non relevant edges while preserving the others.