A fast and effective outlier detection method for matching uncalibrated images

Many image analysis tasks require an outlier detection procedure to identify the false matches. In this paper, a fast and effective outlier detection method is presented to match images in the uncalibrated case. This method employs a hypothesis test on the consistency of dominant orientations of the feature points to significantly increase the detection speed. Moreover, it can also effectively find the outliers that can not be identified by traditional RANSAC-based methods using epipolar constraint. Note that our method does not require the prior knowledge of camera parameters or the percentage of outliers. The experimental results show that our method outperforms the classical RANSAC-based methods both in speed and in accuracy of the results.