22 March 1999 Image interpretation with a semantic graph: labeling over-segmented images and detection of unexpected objects
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Abstract
With the AC4 algorithm proposed by Mohr and Henderson in 1986, a framework was proposed to solve the problem of matching between data and a semantic graph. This matching is usually associated with the notion of understanding. However, the very high combinatorial aspect of this problem makes very difficult to solve it by a computer. Moreover, there are very few bijective relations in practice. The high combinatorial aspect can be reduced with a local checking of the constraint satisfaction. With the AC4 algorithm, this strategy can be used only when the matching is bijective. With the notion of FDCSPBC this strategy was extended to non bijective relations. This case is encountered when we want to label over-segmented images. However, this extension is only adequate for a matching corresponding to surjective functions. The question of new extensions of this strategy to non surjective functions and non functional relations can be considered. In medical image analysis, the second case is often encountered when an unexpected object like a tumor appears. In that case, the data can not be mapped to the semantic graph, with a classical approach. In this paper we propose an extension of the FDCSPBC to solve the constraint satisfaction problem for non functional relations.
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Aline Deruyver, Aline Deruyver, Yann Hode, Yann Hode, } "Image interpretation with a semantic graph: labeling over-segmented images and detection of unexpected objects", Proc. SPIE 3722, Applications and Science of Computational Intelligence II, (22 March 1999); doi: 10.1117/12.342898; https://doi.org/10.1117/12.342898
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