Statistically independent region models applied to correlation and segmentation techniques

Philippe Refregier; Francois Goudail; Christophe Chesnaud

doi:10.1117/12.365909

2 June 1999 Statistically independent region models applied to correlation and segmentation techniques

Philippe Refregier, Francois Goudail, Christophe Chesnaud

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Proceedings Volume 10296, 1999 Euro-American Workshop Optoelectronic Information Processing: A Critical Review; 102960C (1999) https://doi.org/10.1117/12.365909
Event: Euro-American Workshop on Optoelectronic Information Processing, 1999, Colmar, France

Abstract

Recently new approaches for location and/or segmentation of objects with unknown gray levels embedded in non-overlapping noise have been proposed. These techniques are based on the Statistically Independent Region (SIR) model and are optimal in the maximum likelihood sense. In this paper, we review their theoretical bases and propose a unified approach which enlarges their field of application.

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Philippe Refregier, Francois Goudail, and Christophe Chesnaud "Statistically independent region models applied to correlation and segmentation techniques", Proc. SPIE 10296, 1999 Euro-American Workshop Optoelectronic Information Processing: A Critical Review, 102960C (2 June 1999); https://doi.org/10.1117/12.365909

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KEYWORDS

Image segmentation

Statistical modeling

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