Graph Based Representations in Pattern Recognition by Ch. Demko (auth.), Univ.-Prof. Dr. Jean-Michel Jolion,

By Ch. Demko (auth.), Univ.-Prof. Dr. Jean-Michel Jolion, Univ.-Prof. Dr. Walter G. Kropatsch (eds.)

Graph-based illustration of pictures is changing into a well-liked software because it represents in a compact approach the constitution of a scene to be analyzed and makes it possible for a simple manipulation of sub-parts or of relationships among elements. as a result, it really is customary to manage different degrees from segmentation to interpretation. The 14 papers during this quantity are grouped within the following topic parts: hypergraphs, popularity and detection, matching, segmentation, implementation difficulties, representation.

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The solution is guaranteed to be the minimum cost path, provided that h is inferior or at least equal to the exact distance remaining to reach the goal node. 2. Representations Different representations can be chosen: for instance, Martelli proposed to map the nodes to the frontier between two pixels [7]. For convenient reasons, in particular the fact that the result can be displayed as an edge map, we prefer to match directly the nodes with the pixels of the image: - The starting node can be selected among pixels with high gradient values.

When examining a state in the search process, the algorithm checks if there is a syntactic transformation that can be applied to it. For each applicable transformation, a new state s is added to the SSR graph. In Fig. 5 the states generated by syntactic transformations are not explicitly shown, but their presence is indicated by labeling the state to which the transformation has been applied. The nodes involved in a syntactic transformation are marked, in order to avoid reconsidering them in succesive transformations.

Computer-vision. , New Jersey, 1982. [3) Ballard, D. H. : A ladder-structured decision tree for recognizing tumors in chest radiographs. IEEE Trans. Compo25, 503- 513 (1976). : Using Canny's criteria to derive a recursively implemented optimal edge detector. Int. J. Comput. Vision 1, 167- 187 (1987). [5) Lester, J. , Williams, H. , Weintraub, B. , Brenner, J. F. Two graph searching techniques for boundary finding in white blood cell images. Compo BioI. Med. 8, 293- 308 (1978). : Edge detection using heuristic search methods.

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