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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.

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Results in the first data set. (a) Original images. (b) Foreground extracted with RC [13]. (c) Foreground extracted with TSS [14]. (d) Foreground extracted with FMDOG.
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fig3: Results in the first data set. (a) Original images. (b) Foreground extracted with RC [13]. (c) Foreground extracted with TSS [14]. (d) Foreground extracted with FMDOG.

Mentions: Some excellent results in the first data set are shown in Figure 3 and the other excellent results in the second data set are shown in Figure 4. Observations on Figures 3 and 4, our proposed algorithm, can extract the foreground beyond 95%. The original images in Figure 3 have rich color and texture in the foreground. When we use the DoG to check out the candidate keypoints, the number of keypoints in the foreground is larger than in the background. In this case, it is easy to extract the foreground. However, few of the images in the first data set are difficult to extract foreground automatically because there is confusion between foreground and background. For the images with outstanding target and complicated background, although more keypoints in the background are obtained, we can get effective keypoints by the keypoints filtered function. So we can extract the foreground with high performance, for example, the lady, the dog, and the postbox.


Automatic foreground extraction based on difference of Gaussian.

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

Results in the first data set. (a) Original images. (b) Foreground extracted with RC [13]. (c) Foreground extracted with TSS [14]. (d) Foreground extracted with FMDOG.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4127285&req=5

fig3: Results in the first data set. (a) Original images. (b) Foreground extracted with RC [13]. (c) Foreground extracted with TSS [14]. (d) Foreground extracted with FMDOG.
Mentions: Some excellent results in the first data set are shown in Figure 3 and the other excellent results in the second data set are shown in Figure 4. Observations on Figures 3 and 4, our proposed algorithm, can extract the foreground beyond 95%. The original images in Figure 3 have rich color and texture in the foreground. When we use the DoG to check out the candidate keypoints, the number of keypoints in the foreground is larger than in the background. In this case, it is easy to extract the foreground. However, few of the images in the first data set are difficult to extract foreground automatically because there is confusion between foreground and background. For the images with outstanding target and complicated background, although more keypoints in the background are obtained, we can get effective keypoints by the keypoints filtered function. So we can extract the foreground with high performance, for example, the lady, the dog, and the postbox.

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