In this paper, we introduce an image combinatorial model based on hypergraph theory. Hypergraph theory is an efficient graphic connectivity analysis [14], pattern analysis [23], formal frame for developing image processing applications such artificial intelligence [13], and computer vision [21]. As for as segmentation. Under the assumption that a hypergraph satisstructural relations, in numerous situations, graphs have fies the Helly property, we develop a segmentation algorithm turned out to provide the most appropriate tool for setting that partitions the image by inspecting packets of pixels. This up the mathematical model. This is certainly one reason process is controlled by a homogeneity criterion. We also preswhy graph theory has expanded so rapidly during the past ent a preprocessing algorithm that ensures that the hypergraph decades [16].
associated with any image satisfies the Helly property. We show
The main drawback of proximity graphs is their use of that the algorithm is convergent. A performance analysis of binary neighborhood relations. Although binary relations the model and of the segmentation algorithm is included. Β© 1997 are relevant for many basic situations, they cannot appre-
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hend the structuration process of objects of an arbitrary nature. An image is an organization of objects in a space, but it cannot be disconnected from the human visual system