Edge Detection and Segmentation of Textured Plane Images
We use a Markov framework for finding edges and for partitioning scenes into homogeneous regions. The images are airplane images with a fine resolution. They have been chosen according to the presence of textures, some of these textures being macro-textures. We are working in a supervised context, and we assume the existence of samples for each of the textures.
Segmenting these textures, and due to their resolution, the problem of edge detection proves to be very important. The definition of edges between textures may not always be straightforward since these edges are sometimes materialized by fences, or roads, but some other times, they are only implicit. We use a statistical definition of the edges, and then, after positioning them, we extract informations for the segmentation. The use of these informations improves greatly the results.
At the same time, one of our main purpose is to keep the computation time within reasonable bounds. This was done by selecting carefully the model energy.
KeywordsSimulated Annealing Energy Function Edge Detection Pattern Anal Markov Random Field
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