Convergent matching for model-based computer vision
We consider matching in model-based computer vision as a converging discrete iteration and give a basis for examining the convergence as the movement of the working point in a lattice. Because the matching is non-deterministic we discuss convergence in terms of completing sub-problems within a time slot. This form of low-level scheduling avoids effectively unlimited trials of sub-graphs, a phenomenon that we call the NP-trap. We define high-level scheduling as the need to test each reference class at least once and thereafter focus attention on the most promising candidates. Examples show the bounding of matching time with a time slot and focusing of attention guided by a figure of merit.
relational matching graph matching model directed recognition structural methods discrete convergence