Abstract
We analyze the dense matching problem for Internet scene images based on the fact that commonly only part of images can be matched due to the variation of view angle, motion, objects, etc. We thus propose regional foremost matching to reject outlier matching points while still producing dense high-quality correspondence in the remaining foremost regions. Our system initializes sparse correspondence, propagates matching with model fitting and optimization, and detects foremost regions robustly. We apply our method to several applications, including time-lapse sequence generation, Internet photo composition, automatic image morphing, and automatic rephotography.
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