我的第一篇paper


找實習雖然只為自己爭取到一個秋季的綠色通道,但可喜的是,我投的幾篇paper,終於中了一篇。

現在可以在英文數據庫或google scholar上面搜索到自己名字,感覺很nice,研究生的心願算是完成了一部分,至於剩下的中不中,都不那么重要了,已經留下了自己在科研道路上的足跡。

投遞的雜志是Signal Processing,是一個很不錯的雜志,從ACCEPT到文章上線速度很快,在我校的評級是B類期刊,發表一篇達到學校博士畢業的基本要求(一篇B或者2篇C),審稿周期算是中等吧,這篇文章的周期大約是7個月

Science Direct

Google Scholar

Automatic image segmentation using salient key point extraction and star shape prior

Xiangli Liao, Hongbo Xu, Yicong Zhou, Kunqian Li, Wenbing Tao, Qiuju Guo, Liman Liu

ARTICLE INFO

Article history:Received 27 September 2013Received in revised form28 March 2014Accepted 29 April 2014

Please cite this article as: X. Liao, et al., Automatic image segmentation using salient key point extraction and star
shape prior, Signal Processing (2014)

ABSTRACT

In this paper, a new unsupervised segmentation method is proposed. The method integrates the star shape prior of the image object with salient point detection algorithm. In the proposed method, the Harris salient point detection is first applied to the color image to obtain the initial salient points. A regional contrast based saliency extraction method is then used to select rough object regions in the image. To restrict the distribution of salient points, an adaptive threshold segmentation is applied to the saliency map to get the saliency mask. And then the salient region points can be obtained by placing the saliency mask on the initial Harris salient points. In order to make sure the salient points which we get are inside the image object thus the star shape constraint can be applied to the graph cuts segmentation, the Affinity Propagation (AP) clustering is employed to find the salient key points among the salient region points. Finally, these salient key points are regarded as foreground seeds and the star shape prior is introduced to graph cuts segmentation framework to extract the foreground object. Extensive experiments and comparisons on public database are provided to demonstrate the good performance of the proposed method.&2014 Published by Elsevier B.V.

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