Segmentation Through Edge-linking - Segmentation for Video-based Driver Assistance Systems

This work aims to develop an image segmentation method to be used in automotive driver assistance systems. In this context it is possible to incorporate a priori knowledge from other sensors to ease the problem of localizing objects and to improve the results. It is however desired to produce accurate segmentations displaying good edge localization and to have real time capabilities. An edge-segment grouping method is presented to meet these aims. Edges of varying strength are detected initially. In various preprocessing steps edge-segments are formed. A sparse graph is generated from those using perceptual grouping phenomena. Closed contours are formed by solving the shortest path problem. Using test data fitting to the application domain, it is shown that the proposed method provides more accurate results than the well-known Gradient Vector Field Snakes.