Automatic Foreground Extraction Based on Difference of Gaussian

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...

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Detalles Bibliográficos
Autores principales: Yuan, Yubo, Liu, Yun, Dai, Guanghui, Zhang, Jing, Chen, Zhihua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4127285/
https://www.ncbi.nlm.nih.gov/pubmed/25136655
http://dx.doi.org/10.1155/2014/296074
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author Yuan, Yubo
Liu, Yun
Dai, Guanghui
Zhang, Jing
Chen, Zhihua
author_facet Yuan, Yubo
Liu, Yun
Dai, Guanghui
Zhang, Jing
Chen, Zhihua
author_sort Yuan, Yubo
collection PubMed
description 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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spelling pubmed-41272852014-08-18 Automatic Foreground Extraction Based on Difference of Gaussian Yuan, Yubo Liu, Yun Dai, Guanghui Zhang, Jing Chen, Zhihua ScientificWorldJournal Research Article 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. Hindawi Publishing Corporation 2014 2014-07-20 /pmc/articles/PMC4127285/ /pubmed/25136655 http://dx.doi.org/10.1155/2014/296074 Text en Copyright © 2014 Yubo Yuan et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Yuan, Yubo
Liu, Yun
Dai, Guanghui
Zhang, Jing
Chen, Zhihua
Automatic Foreground Extraction Based on Difference of Gaussian
title Automatic Foreground Extraction Based on Difference of Gaussian
title_full Automatic Foreground Extraction Based on Difference of Gaussian
title_fullStr Automatic Foreground Extraction Based on Difference of Gaussian
title_full_unstemmed Automatic Foreground Extraction Based on Difference of Gaussian
title_short Automatic Foreground Extraction Based on Difference of Gaussian
title_sort automatic foreground extraction based on difference of gaussian
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4127285/
https://www.ncbi.nlm.nih.gov/pubmed/25136655
http://dx.doi.org/10.1155/2014/296074
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AT daiguanghui automaticforegroundextractionbasedondifferenceofgaussian
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AT chenzhihua automaticforegroundextractionbasedondifferenceofgaussian

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