Weighted color and texture sample selection for image matting
Color information is leveraged by color sampling-based matting methods to find the best known samples for foreground and background color of unknown pixels. Such methods do not perform well if there is an overlap in the color distribution of foreground and background regions because color cannot dis...
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sg-ntu-dr.10356-984232020-05-28T07:41:42Z Weighted color and texture sample selection for image matting Shahrian, Ehsan Rajan, Deepu School of Computer Engineering IEEE Conference on Computer Vision and Pattern Recognition (2012 : Providence, Rhode Island, US) DRNTU::Engineering::Computer science and engineering Color information is leveraged by color sampling-based matting methods to find the best known samples for foreground and background color of unknown pixels. Such methods do not perform well if there is an overlap in the color distribution of foreground and background regions because color cannot distinguish between these regions and hence, the selected samples cannot reliably estimate the matte. Similarly, alpha propagation based matting methods may fail when the affinity among neighboring pixels is reduced by strong edges. In this paper, we overcome these two problems by considering texture as a feature that can complement color to improve matting. The contribution of texture and color is automatically estimated by analyzing the content of the image. An objective function containing color and texture components is optimized to choose the best foreground and background pair among a set of candidate pairs. Experiments are carried out on a benchmark data set and an independent evaluation of the results show that the proposed method is ranked first among all other image matting methods. 2013-07-29T08:33:03Z 2019-12-06T19:55:07Z 2013-07-29T08:33:03Z 2019-12-06T19:55:07Z 2012 2012 Conference Paper Shahrian, E., & Rajan, D. (2012). Weighted color and texture sample selection for image matting. 2012 IEEE Conference on Computer Vision and Pattern Recognition. https://hdl.handle.net/10356/98423 http://hdl.handle.net/10220/12501 10.1109/CVPR.2012.6247741 en © 2012 IEEE. |
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DRNTU::Engineering::Computer science and engineering Shahrian, Ehsan Rajan, Deepu Weighted color and texture sample selection for image matting |
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Color information is leveraged by color sampling-based matting methods to find the best known samples for foreground and background color of unknown pixels. Such methods do not perform well if there is an overlap in the color distribution of foreground and background regions because color cannot distinguish between these regions and hence, the selected samples cannot reliably estimate the matte. Similarly, alpha propagation based matting methods may fail when the affinity among neighboring pixels is reduced by strong edges. In this paper, we overcome these two problems by considering texture as a feature that can complement color to improve matting. The contribution of texture and color is automatically estimated by analyzing the content of the image. An objective function containing color and texture components is optimized to choose the best foreground and background pair among a set of candidate pairs. Experiments are carried out on a benchmark data set and an independent evaluation of the results show that the proposed method is ranked first among all other image matting methods. |
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School of Computer Engineering |
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School of Computer Engineering Shahrian, Ehsan Rajan, Deepu |
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Conference or Workshop Item |
author |
Shahrian, Ehsan Rajan, Deepu |
author_sort |
Shahrian, Ehsan |
title |
Weighted color and texture sample selection for image matting |
title_short |
Weighted color and texture sample selection for image matting |
title_full |
Weighted color and texture sample selection for image matting |
title_fullStr |
Weighted color and texture sample selection for image matting |
title_full_unstemmed |
Weighted color and texture sample selection for image matting |
title_sort |
weighted color and texture sample selection for image matting |
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2013 |
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https://hdl.handle.net/10356/98423 http://hdl.handle.net/10220/12501 |
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1681058115633545216 |