Meaningful Alignments

Authors:
Agnès Desolneux;Lionel Moisan;Jean-Michel Morel
Affiliations:
CMLA, ENS Cachan, 61 Avenue du Président Wilson, 94235 Cachan Cedex, France;CMLA, ENS Cachan, 61 Avenue du Président Wilson, 94235 Cachan Cedex, France;CMLA, ENS Cachan, 61 Avenue du Président Wilson, 94235 Cachan Cedex, France
Venue:
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Year:
2000

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Abstract

We propose a method for detecting geometric structures in an image, without any a priori information. Roughly speaking, we say that an observed geometric event is “meaningful” if the expectation of its occurences would be very small in a random image. We discuss the apories of this definition, solve several of them by introducing “maximal meaningful events” and analyzing their structure. This methodology is applied to the detection of alignments in images.