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 suer from problems in practical implementations. Generate-and-test methods are sensitive to noise in the data. They often fail when the generated model t is poor due to error in the selected features. Hough transform variations are somewhat less sensitive the noise, but implementations for complex problems suer 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 t to data that draws benets 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 ecient generate-and-test techniques to be used, including the use of randomization to lim...

• Type: Conference
• Year: 1999
• Where: ICCVW
• Authors: Clark F. Olson
• Keywords: Computer Vision, Hough Transform, Hough Transform Variations, ICCVW 1999, Methods Su Er