The success of skeletal model in object recognition from segmented images motivates the development of a skeletal model for top-down object recognition and segmentation. We propose a novel skeleton-based generative shape model which is suitable for efficient search using dynamic programming (DP). We have devised an exclusion principle enabling DP to discover multiple instances of an object category in one pass. Finally, we have improved an oriented chamfer distance for rank-ordering generated hypotheses. Improved or comparable recognition and segmentation results are reported on the ETHZ data set.
Instead of object's contour, Trinh and Kimia REF use skeleton-based generative shape model with modified dynamic programming to detect objects.
Category-Specific Object Recognition and Segmentation Using a Skeletal Shape Model
BMVC
Computer Science