Improving non-rigid point matching with a new model

In non-rigid point matching, there are, two difficulties: how to implement good correspondence and to suppress bias. We propose improving robust mixture model using the t distribution and combining the improved model with TPS (Thin Plate Splines) technique so as to deal effectively with the two above-mentioned difficulties. The robust non-rigid point matching algorithm and four subtopics including robust mixture model using the t-distribution, non-rigid point matching maximum a priori estimation and EM (Expectation Maximization) algorithm, non-rigid mapping based on TPS, and algorithm flowchart are proposed. Test results show that the above-mentioned two difficult problems in non-rigid point matching can be solved effectively; the matching resolution can be further improved by using a model choice criterion in DA (Deterministic Annealing) process.