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Proceedings Paper

Saliency based line grouping for structure detection
Author(s): Sandra Denasi; Paolo Magistris; Giorgio Quaglia
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Paper Abstract

The formulation of object hypotheses for recognition requires the localization of the most meaningful groups of lines in the image, that correspond to the elementary structures used to describe and represent objects or some parts of them. This paper describes an approach where the most salient line segments are used to suggest main structures. A measure of saliency is proposed on the basis of length and luminance contrast of line segments. In order to enhance the performance of the algorithm, only a reduced number of line segments are taken into account to formulate rough structures. These significant lines are ordered according to their saliency and the contour map is analyzed in a coarse to fine sequence. Then a grouping strategy based on perceptual organization criteria extracts closed polygons, paying special attention to quadrangles and triangles, C-shaped and L-shaped structures. The saliency measure allows the grouping process to focus on the bigger and/or more evident structures, giving priority to a coarse aggregation. Besides a fine aggregation is performed at the same time to reinforce and refine each coarse aggregation step. This cooperation allows a sophisticated usage of spatial thresholds, that reduces the direct impact of specific threshold values making grouping process less sensitive to the scale of the objects present in the image.

Paper Details

Date Published: 10 October 1994
PDF: 12 pages
Proc. SPIE 2353, Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision, (10 October 1994); doi: 10.1117/12.188897
Show Author Affiliations
Sandra Denasi, Istituto Elettrotecnico Nazionale Galileo Ferraris (Italy)
Paolo Magistris, Istituto Elettrotecnico Nazionale Galileo Ferraris (Italy)
Giorgio Quaglia, Istituto Elettrotecnico Nazionale Galileo Ferraris (Italy)

Published in SPIE Proceedings Vol. 2353:
Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision
David P. Casasent, Editor(s)

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