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 chi squared 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 discarded. Experiments show this technique to be particularly effective, typically lowering the percentage of outliers from around 35% to less than 5%, permitting direct model fitting rather than random sampling techniques for any further analysis.
