Monocular depth ordering using perceptual occlusion cues

Abstract

Comunicació presentada al congrés International Conference on Computer Vision Theory and Applications celebrat del 27 al 29 de febrer de 2016 a Roma, Itàlia.

In this paper we propose a method to estimate a global depth order between the objects of a scene using information from a single image coming from an uncalibrated camera. The method we present stems from early vision cues such as occlusion and convexity and uses them to infer both a local and a global depth order. Monocular occlusion cues, namely, T-junctions and convexities, contain information suggesting a local depth order between neighbouring objects. A combination of these cues is more suitable, because, while information conveyed by T-junctions is perceptually stronger, they are not as prevalent as convexity cues in natural images. We propose a novel convexity detector that also establishes a local depth order. The partial order is extracted in T-junctions by using a curvature-based multi-scale feature. Finally, a global depth order, i.e., a full order of all shapes that is as consistent as possible with the computed partial orders that can tolerate conflicting partial or ders is computed. An integration scheme based on a Markov chain approximation of the rank aggregation problem is used for this purpose. The experiments conducted show that the proposed method compares favorably with the state of the art.

The authors acknowledge partial support by MICINN project, reference MTM2012-30772, and by GRC reference 2014 SGR 1301, Generalitat de Catalunya.