Effective Corner Matching英文文献资料.pdf

Effective Corner Matching P. Smith , D. Sinclair , R. Cipolla
and K. Wood
Department of Engineering, University of Cambridge, Cambridge, UK
Olivetti and Oracle Research Laboratory, Cambridge, UK pas1001@eng.cam.ac.uk Abstract This paper tackles the problem of obtaining a good initial set of corner matches between two images without resorting to any constraints from motion or structure models. Several different matching metrics, both traditional and statistical, are evaluated and the effect of matching using sub-pixel information is studied. It is found that, in most cases, the commonly-used cross-correlation does not perform as well as some other measures, such as the  test or the sum of squared differences, and that it is essential to use sub-pixel accuracy if mismatches are to be avoided. Further, a new technique, the Median Flow Filter, is introduced. This detects outliers by assuming that the image motion is locally similar. Any matches which are in gross disagreement with the local “median flow” are dis- carded. Experiments show this technique to be particularly effective, typically lowering the percentage of outliers from around 35% to less than 5%, per- mitting direct model fitting rather than random sampling techniques for any further analysis. 1 Introduction Feature matching is a key component in many computer vision applications, for example stereo vision, motion tracking, and identification. Feature matches may be sufficient alone, but are also an ideal platform for “bootstrapping” denser and more complex analysis of images. Of all possible features, “corners” are the most widely used; their two-dimensional structure providing the most information about im