Contour Detection and Characterization for Asynchronous Event Sensors

Abstract

The bio-inspired, asynchronous event-based dynamic vision sensor records temporal changes in the luminance of the scene at high temporal resolution. Since events are only triggered at significant luminance changes, most events occur at the boundary of objects and their parts. The detection of these contours is an essential step for further interpretation of the scene. This paper presents an approach to learn the location of contours and their border ownership using Structured Random Forests on event-based features that encode motion, timing, texture, and spatial orientations. The classifier integrates elegantly information over time by utilizing the classification results previously computed. Finally, the contour detection and boundary assignment are demonstrated in a layer-segmentation of the scene. Experimental results demonstrate good performance in boundary detection and segmentation.