In this paper, we address the problem of the object recognition in a complex 3-D scene by detecting the 2-D object projection on the image-plane for an autonomous vehicle driving; in particular, the problems of road detection and obstacle avoidance in natural road scenes are investigated. A new implementation of the Hough Transform (HT), called Labeled Hough Transform (LHT), to extract and group symbolic features is here presented; the novelty of this method, in respect to the traditional approach, consists in the capability of splitting a maximum in the parameter space into noncontiguous segments, while performing voting. Results are presented on a road image containing obstacles which show the efficiency, good quality, and time performances of the algorithm.
HOUGH-BASED RECOGNITION OF COMPLEX 3D ROAD SCENES
Regazzoni, C. S.
1992-01-01
Abstract
In this paper, we address the problem of the object recognition in a complex 3-D scene by detecting the 2-D object projection on the image-plane for an autonomous vehicle driving; in particular, the problems of road detection and obstacle avoidance in natural road scenes are investigated. A new implementation of the Hough Transform (HT), called Labeled Hough Transform (LHT), to extract and group symbolic features is here presented; the novelty of this method, in respect to the traditional approach, consists in the capability of splitting a maximum in the parameter space into noncontiguous segments, while performing voting. Results are presented on a road image containing obstacles which show the efficiency, good quality, and time performances of the algorithm.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
