Authors:
Andreas Laika
1
;
Adrian Taruttis
2
and
Walter Stechele
2
Affiliations:
1
BMW Group Forschung und Technik, Germany
;
2
Technische Universität München, Germany
Keyword(s):
Segmentation, Edge linking, Driver assistance, Object recognition.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Coding and Compression
;
Image Enhancement and Restoration
;
Image Formation and Preprocessing
;
Imaging in Computing and Business (Document Imaging, Metadata, Quality Control)
Abstract:
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.