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Automatic foreground extraction based on difference of Gaussian.

Yuan Y, Liu Y, Dai G, Zhang J, Chen Z - ScientificWorldJournal (2014)

Bottom Line: In our algorithm, DoG is employed to find the candidate keypoints of an input image in different color layers.Finally, Normalized cut (Ncut) is used to segment an image into several regions and locate the foreground with the number of keypoints in each region.Experiments on the given image data set demonstrate the effectiveness of our algorithm.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.

ABSTRACT
A novel algorithm for automatic foreground extraction based on difference of Gaussian (DoG) is presented. In our algorithm, DoG is employed to find the candidate keypoints of an input image in different color layers. Then, a keypoints filter algorithm is proposed to get the keypoints by removing the pseudo-keypoints and rebuilding the important keypoints. Finally, Normalized cut (Ncut) is used to segment an image into several regions and locate the foreground with the number of keypoints in each region. Experiments on the given image data set demonstrate the effectiveness of our algorithm.

Show MeSH
Basic procedure of the keypoints filter. (a) Initial result of difference of Gaussian (DoG). (b) Candidate points we get through the 5 × 5 filter. (c) Information of edges. (d) Information of edges on the original image. (e) Result of candidate points rebuild.
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fig2: Basic procedure of the keypoints filter. (a) Initial result of difference of Gaussian (DoG). (b) Candidate points we get through the 5 × 5 filter. (c) Information of edges. (d) Information of edges on the original image. (e) Result of candidate points rebuild.

Mentions: We regard the candidate points which are obtained from the above steps as keypoints. At last, we locate the foreground by the number of keypoints in each region. The process of filtering keypoints is displayed in Figure 2.


Automatic foreground extraction based on difference of Gaussian.

Yuan Y, Liu Y, Dai G, Zhang J, Chen Z - ScientificWorldJournal (2014)

Basic procedure of the keypoints filter. (a) Initial result of difference of Gaussian (DoG). (b) Candidate points we get through the 5 × 5 filter. (c) Information of edges. (d) Information of edges on the original image. (e) Result of candidate points rebuild.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4127285&req=5

fig2: Basic procedure of the keypoints filter. (a) Initial result of difference of Gaussian (DoG). (b) Candidate points we get through the 5 × 5 filter. (c) Information of edges. (d) Information of edges on the original image. (e) Result of candidate points rebuild.
Mentions: We regard the candidate points which are obtained from the above steps as keypoints. At last, we locate the foreground by the number of keypoints in each region. The process of filtering keypoints is displayed in Figure 2.

Bottom Line: In our algorithm, DoG is employed to find the candidate keypoints of an input image in different color layers.Finally, Normalized cut (Ncut) is used to segment an image into several regions and locate the foreground with the number of keypoints in each region.Experiments on the given image data set demonstrate the effectiveness of our algorithm.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.

ABSTRACT
A novel algorithm for automatic foreground extraction based on difference of Gaussian (DoG) is presented. In our algorithm, DoG is employed to find the candidate keypoints of an input image in different color layers. Then, a keypoints filter algorithm is proposed to get the keypoints by removing the pseudo-keypoints and rebuilding the important keypoints. Finally, Normalized cut (Ncut) is used to segment an image into several regions and locate the foreground with the number of keypoints in each region. Experiments on the given image data set demonstrate the effectiveness of our algorithm.

Show MeSH