Abstract
We present a data-driven method to predict the performance of an image completion method.
Our image completion method is based on the state-of-the-art non-parametric framework of
Wexler et al. [2007]. It uses automatically derived search space constraints for
patch source regions, which lead to improved texture synthesis and semantically more
plausible results. These constraints also facilitate performance prediction by allowing
us to correlate output quality against features of possible regions used for synthesis.
We use our algorithm to first crop and then complete stitched panoramas. Our predictive
ability is used to find an optimal crop shape before the completion is computed,
potentially saving significant amounts of computation. Our optimized crop includes as
much of the original panorama as possible while avoiding regions that can be less
successfully filled in. Our predictor can also be applied for hole filling in the
interior of images. In addition to extensive comparative results, we ran several user
studies validating our predictive feature, good relative quality of our results against
those of other state-of-the-art algorithms, and our automatic cropping algorithm.
@article{Kopf2012,
author = {Johannes Kopf and Wolf Kienzle and Steven Drucker and Sing Bing Kang},
title = {Quality Prediction for Image Completion},
journal = {ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2012)},
year = {2012},
volume = {31},
number = {6},
pages = {to appear}
}
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