Model selection techniques and merging rules for range data segmentation algorithms

The problem of model selection is relevant to many areas of computer vision. Model selection criteria have been used in the vision literature and many more have been proposed in statistics, but the relative strengths of these criteria have not been analyzed in vision. More importantly, suitable extensions to these criteria must be made to solve the problems unique to computer vision. Using the problem of surface reconstruction as our context, we analyze existing criteria using simulations and sensor data, introduce new criteria from statistics, develop novel criteria capable of handling unknown error distributions and outliers, and extend model selection criteria to apply to the surface merging problem. The new and existing model selection criteria and merging rules are tested lover a wide range of experimental conditions using both synthetic and sensor data. The new surface merging rules improve upon previous results and work well, even at small step heights (h = 2 sigma) and at crease discontinuities. Our results show that a Bayesian criterion and its bootstrapped Variant perform the best, although for time-sensitive applications, a Variant of the Akaike criterion may be a better choice. Unfortunately, none of the criteria work reliably for small region sizes, implying that model selection and surface merging should be avoided unless the region size is sufficiently large. (C) 2000 Academic Press.