Robust Feature Point Matching in General Multi-Image Setups

We present a robust feature matching approach that considers features from more than two images during matching. Traditionally, corners or feature points are matched between pairs of images. Starting from one image, corresponding features are searched in the other image. Yet, often this two-image matching is only a subproblem and actually robust matches over multiple views and / or images acquired at several instants in time are required. In our feature matching approach we consider the multi-view video data modality and find matches that are consistent in three images. Requiring neither calibrated nor synchronized cameras, we are able to reduce the percentage of wrongly matched features considerably. We evaluate the approach for differentfeaturedetectorsandtheirnaturaldescriptorsandshowanapplicationofourimprovedmatchingapproachforoptical flow calculation onunsynchronized stereosequences

Robust Feature Point Matching in General Multi-Image Setups

We present a robust feature matching approach that considers features from more than two images during matching. Traditionally, corners or feature points are matched between pairs of images. Starting from one image, corresponding features are searched in the other image. Yet, often this two-image matching is only a subproblem and actually robust matches over multiple views and / or images acquired at several instants in time are required. In our feature matching approach we consider the multi-view video data modality and find matches that are consistent in three images. Requiring neither calibrated nor synchronized cameras, we are able to reduce the percentage of wrongly matched features considerably. We evaluate the approach for different feature detectors and their natural descriptors and show an application of our improved matching approach for optical flow calculation on unsynchronized stereo sequences. Keywords: Keypoint matching, motion estimation, multi-view video