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Proceedings Paper

Automatic detection of signal-breaking stationarity by analysis of its transitions and modelization of its density of probability: application to roof and step-edge detection in range images
Author(s): Serge Reboul; Ludovic Evrard; Jean-Paul Dubus
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Paper Abstract

In this article we describe a new approach of brooking stationary detection in a noisy signal. We consider the signal corrupted by an additive stationary noise, whose form of the density of probability is none. This new approach leans on the detection and the analysis of the transitions of the signal. The extraction of transition in the signal associates, to each sample in the transition, a constant value equal to the surface of the signal in the transition. The application of this operator to the gradient of the signal gives us the value of the transitions dynamics in the signal. The dynamics transitions density probability modelization by a parametric curve allows us to deduce the level of noise in the signal. It is from the level of noise that we determine a threshold on the height of the transitions. Then we consider the transitions smaller than the threshold as transitions of the noise and the transitions higher than the threshold as the variations of the signal without noise. THis technique of rupture detection is entirely automatic and self adapted to the level of noise in the signal. We present the study and the implementation of this global approach for the detection of roof and step edges in range images. We detect roof and step edge in the image with the signals of transition extracted in two orthogonal directions.

Paper Details

Date Published: 4 April 1997
PDF: 11 pages
Proc. SPIE 3026, Nonlinear Image Processing VIII, (4 April 1997); doi: 10.1117/12.271115
Show Author Affiliations
Serge Reboul, Univ. du Littoral (France)
Ludovic Evrard, Univ. du Littoral (France)
Jean-Paul Dubus, Univ. des Sciences et des Technologies de Lille (France)


Published in SPIE Proceedings Vol. 3026:
Nonlinear Image Processing VIII
Edward R. Dougherty; Jaakko T. Astola, Editor(s)

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