Part-based Bayesian recognition using implicit polynomial invariants

We present an approach to recognition that is based on partitioning and invariant recognition in a Bayesian framework. The intended application domain is that of complex articulated objects in arbitrary position and under considerable occlusion. First, since the performance of traditional model-based recognition strategies degrades with increasing object data-base size, with partial occlusion, and with articulation, we employ a partitioning that does not rely on apriori primitives or models. Rather, this scheme decomposes segmented shapes into parts, where the form of each part is not known apriori, but is derived based on generic geometric assumptions about objects and their projections. Specifically, two types of parts, neck-based and limb-based, give rise to a shape decomposition that remains invariant under occlusion in the visible portion of the object, unaltered under articulation of parts, is stable under slight changes in viewing geometry and finally is robust with changes in resolution and scale. Second, the parts derived from the first stage are described by implicit polynomial curves. These polynomials represent the parts well and are computationally simple to fit to the data. However, the great advantage in using implicit polynomials is the algebraic invariance associated with them. Each part is represented by a vector of invariants that remains essentially independent of viewing geometry, and as such is suitable for matching purposes. The matching process is a Bayesian engine based on asymptotic distributions. In the conclusion section, we briefly indicate how this technology fits into a complete object recognition system