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Temporal Epipolar Regions

Dynamic events are often photographed by a number of people from different viewpoints at different times, resulting in an unconstrained set of images. Finding the corresponding moving features in each of the images allows us to extract information about objects of interest in the scene. Computing correspondence of moving features in such a set of images is considerably more challenging than computing correspondence in video due to possible significant differences in viewpoints and inconsistent timing between image captures. The prediction methods used in video for improving robustness and efficiency are not applicable to a set of still images. In this paper we propose a novel method to predict locations of an approximately linear moving feature point, given a small subset of correspondences and the temporal order of image captures. Our method extends the use of epipolar geometry to divide images into valid and invalid regions, termed Temporal Epipolar Regions (TERs). We formally prove that the location of a feature in a new image is restricted to valid TERs. We demonstrate the effectiveness of our method in reducing the search space for correspondence on both synthetic and challenging real world data, and show the improved matching.

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