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cqfcsu

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[求助] 请帮忙看看这两篇文章是否被检索?

EI:
文章:A Joint feature space unsupervised matting with visual attention prior
杂志:Journal of Optoelectronics laser (光电子 激光,中文杂志)

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cqfcsu

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引用回帖:
4楼: Originally posted by Godblesswz at 2016-03-30 10:50:34
detailed_3-30-2016_2491982

谢谢
5楼2016-04-01 20:05:40
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Godblesswz

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你这属于不同数据库类型文章,请另外发帖。
The king of data
2楼2016-03-30 10:46:38
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Godblesswz

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【答案】应助回帖

★ ★ ★ ★ ★ ★ ★ ★ ★ ★
感谢参与,应助指数 +1
cqfcsu: 金币+10, ★★★★★最佳答案 2016-04-01 20:06:15
sunshan4379: LS-EPI+1, 感谢应助! 2016-04-01 21:17:05
第一篇 EI 检索

Accession number:       
20161102103181
        Title:        A joint feature space unsupervised matting with visual attention prior
        Authors:        Chen, Qiu-Feng1 Email author chenqiufeng0204@126.com; Shen, Qun-Tai1; Liu, Peng-Fei1, 2
        Author affiliation:        1 School of Information Science and Engineering, Central South University, Changsha, China
                2 Unit 95856 of PLA, Nanjing, China
        Corresponding author:        Chen, Qiu-Feng (chenqiufeng0204@126.com)
        Source title:        Guangdianzi Jiguang/Journal of Optoelectronics Laser
        Abbreviated source title:        Guangdianzi Jiguang
        Volume:        26
        Issue:        12
        Issue date:        December 15, 2015
        Publication year:        2015
        Pages:        2400-2408
        Language:        Chinese
        ISSN:        10050086
        CODEN:        GUJIE9
        Document type:        Journal article (JA)
        Publisher:        Board of Optronics Lasers
        Abstract:        For spectral matting, the components are not compact enough. It's hard to distinguish the foreground components from the background components without heuristic prior. Therefore, this paper presents an unsupervised matting method with visual attention prior. Based on the sorted radial-difference texture operator, the joint color and texture features are abstracted. The features are clustered to obtain the initial components whose dimensions are reduced previously. Taking advantage of the human visual attention prior, the component's center prior, boundary prior and saliency are calculated. Then the foreground and background possibilities of the components are calculated. Based on the foreground and background possibilities, a coarse matte with the lowest matting cost is obtained. Finally, the coarse matte is optimized using the visual prior information and laplacian local information. The results reveal that the proposed algorithm has better integrity and detail preserving ability. © 2015, Board of Optronics Lasers. All right reserved.
        Number of references:        24
        Main heading:        Behavioral research
        Controlled terms:        Electronic equipment - Optical devices
        Uncontrolled terms:        Background components - Color and texture features - Color textures - Foreground-background possibility - Human visual attention - Texture operators - Unsupervised matting - Visual Attention
        Classification code:        741.3 Optical Devices and Systems - 971 Social Sciences
        DOI:        10.16136/j.joel.2015.12.0390
        Database:        Compendex
                Compilation and indexing terms, © 2016 Elsevier Inc.
The king of data
3楼2016-03-30 10:47:38
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Godblesswz

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【答案】应助回帖

detailed_3-30-2016_2491982

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The king of data
4楼2016-03-30 10:50:34
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