Junction detection using probabilistic relaxation

Abstract A novel approach to junction detection using an explicit line finder model and contextual rules is presented. Contextual rules expressing properties of 3D-images (surface orientation discontinuities) limit the number of line intersections interpreted as junctions. A probabilistic relaxation labelling scheme is used to combine the a priori world knowledge represented by contextual rules and the information contained in observed lines. Junctions corresponding to a vertex (V-junctions) and an occlusion (T-junction) of 3D objects are detected and stored in a junciton graph. The information in the junction graph is used to extract higher level features. Results of the most promising method, the polyhedral object face recovery, are briefly discussed. The performance of the junction detection process is demonstrated on images from indoor, outdoor, and industrial environments.

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