Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/137491
Title: Pooling-Based Quantitative Approach to Evaluating Binarization Algorithms
Authors: Maofu Liu;Ya Liu;Zhenguang Liu;Huijun Hu;Wei Fang
Year: 2017
Publisher: IEEE
Abstract: To quantitatively evaluate an image binarization algorithm when the ground-truth binary images are unavailable, the authors propose a quantitative evaluation approach based on pooling. First, the binarized images of a sample image from the image dataset (binarized by 27 commonly used binarization algorithms) are put into a pool. Then the pseudo ground truth--two weight matrices of foreground and background points--is produced using the 27 binarized images. Finally, for the image binarization algorithm to be evaluated, a quantitative evaluation operator is generated from the two weight matrices in the pool. The authors ran experiments using two image datasets, and the results show that this quantitative evaluation approach is effective and practical.
URI: http://localhost/handle/Hannan/137491
volume: 24
issue: 1
More Information: 86,
92
Appears in Collections:2017

Files in This Item:
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7849097.pdf705.27 kBAdobe PDF
Title: Pooling-Based Quantitative Approach to Evaluating Binarization Algorithms
Authors: Maofu Liu;Ya Liu;Zhenguang Liu;Huijun Hu;Wei Fang
Year: 2017
Publisher: IEEE
Abstract: To quantitatively evaluate an image binarization algorithm when the ground-truth binary images are unavailable, the authors propose a quantitative evaluation approach based on pooling. First, the binarized images of a sample image from the image dataset (binarized by 27 commonly used binarization algorithms) are put into a pool. Then the pseudo ground truth--two weight matrices of foreground and background points--is produced using the 27 binarized images. Finally, for the image binarization algorithm to be evaluated, a quantitative evaluation operator is generated from the two weight matrices in the pool. The authors ran experiments using two image datasets, and the results show that this quantitative evaluation approach is effective and practical.
URI: http://localhost/handle/Hannan/137491
volume: 24
issue: 1
More Information: 86,
92
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7849097.pdf705.27 kBAdobe PDF
Title: Pooling-Based Quantitative Approach to Evaluating Binarization Algorithms
Authors: Maofu Liu;Ya Liu;Zhenguang Liu;Huijun Hu;Wei Fang
Year: 2017
Publisher: IEEE
Abstract: To quantitatively evaluate an image binarization algorithm when the ground-truth binary images are unavailable, the authors propose a quantitative evaluation approach based on pooling. First, the binarized images of a sample image from the image dataset (binarized by 27 commonly used binarization algorithms) are put into a pool. Then the pseudo ground truth--two weight matrices of foreground and background points--is produced using the 27 binarized images. Finally, for the image binarization algorithm to be evaluated, a quantitative evaluation operator is generated from the two weight matrices in the pool. The authors ran experiments using two image datasets, and the results show that this quantitative evaluation approach is effective and practical.
URI: http://localhost/handle/Hannan/137491
volume: 24
issue: 1
More Information: 86,
92
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7849097.pdf705.27 kBAdobe PDF