A general method for feature matching and model extraction

Abstract

Popular algorithms for feature matching and model extraction fall into two broad categories, generate-and-test and Hough transform variations. However, both methods suffer from problems in practical implementations. Generate-and-test methods are sensitive to noise in the data. They often fail when the generated model fit is poor due to error in the selected features. Hough transform variations are somewhat less sensitive the noise, but implementations for complex problems suffer from large time and space requirements and the detection of false positives. This paper describes a general method for solving problems where a model is extracted from or fit to data that draws benefits from both generate-and-test methods and those based on the Hough transform, yielding a method superior to both. An important component of the method is the subdivision of the problem into many subproblems. This allows efficient generate-and-test techniques to be used, including the use of randomization to limit the number of subproblems that must be examined. However, the subproblems are solved using pose space analysis techniques similar to the Hough transform, which lowers the sensitivity of the method to noise. This strategy is easy to implement and results in practical algorithms that are efficient and robust. We apply this method to object recognition, geometric primitive extraction, robust regression, and motion segmentation.