Finding Geometric and Relational Structures in An Image

Abstract : We present a method for extracting geometric and relational structures from raw intensity data. On one hand, low-level image processing extracts isolated features. On the other hand, image interpretation uses sophisticated object descriptions in representation frameworks such as semantic networks. We suggest an intermediate-level description between low- and high-level vision. This description is produced by grouping image features into more and more abstract structures. First, we motivate our choice with respect to what should be represented and we stress the limitations inherent with the use of sensory data. Second, we describe our current implementation and illustrate it with various examples.
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Communication dans un congrès
Olivier Faugeras. 1st European Conference on Computer Vision (ECCV'90), Apr 1990, Antibes, France. Springer-Verlag, 427/1990, pp.374--384, 1990, Lecture Notes in Computer Science (LNCS). 〈10.1007/BFb0014886〉
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Radu Horaud, Thomas Skordas, Françoise Veillon. Finding Geometric and Relational Structures in An Image. Olivier Faugeras. 1st European Conference on Computer Vision (ECCV'90), Apr 1990, Antibes, France. Springer-Verlag, 427/1990, pp.374--384, 1990, Lecture Notes in Computer Science (LNCS). 〈10.1007/BFb0014886〉. 〈inria-00589995〉

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