Flexible object recognition in cluttered scenes using relative point distribution models

This paper introduces an edge-based object recognition method that is robust with respect to clutter, occlusion and object deformations. The method combines the use of local features and their spatial relationships to identify the point correspondences between the object-of-interest and the input scene. Local features encode information from their neighbourhood, and this renders them insensitive to noise at a distance. However, they have moderate discriminating power, and the proposed method exploits their spatial structure to compensate for this. Our flexible localisation technique, which is based on point distribution models, makes the method also applicable to deformable objects. The point matching task is formulated as an optimisation problem that is solved using the Viterbi algorithm. The method has been validated on challenging real scenes.