Videoscapes: exploring sparse, unstructured video collections
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Integrating approximate depth data into dense image correspondence estimation
Proceedings of the 9th European Conference on Visual Media Production
Preference and artifact analysis for video transitions of places
ACM Transactions on Applied Perception (TAP) - Special issue SAP 2013
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We present an algorithm for estimating dense image correspondences. Our versatile approach lends itself to various tasks typical for video post-processing, including image morphing, optical flow estimation, stereo rectification, disparity/depth reconstruction and baseline adjustment. We incorporate recent advances in feature matching, energy minimization, stereo vision and data clustering into our approach. At the core of our correspondence estimation we use Efficient Belief Propagation for energy minimization. While state-of-the-art algorithms only work on thumbnail-sized images, our novel feature downsampling scheme in combination with a simple, yet efficient data term compression can cope with high-resolution data. The incorporation of SIFT features into data term computation further resolves matching ambiguities, making long-range correspondence estimation possible. We detect occluded areas by evaluating the correspondence symmetry, we further apply Geodesic matting to automatically in paint these regions.