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| 論文名稱 | Genetic cuts for image segmentation |
| 發表日期 | 2014-09-17 |
| 論文收錄分類 | SCI |
| 所有作者 | Shiueng-Bien Yang |
| 作者順序 | 第一作者 |
| 通訊作者 | 是 |
| 刊物名稱 | Genetic cuts for image segmentation |
| 發表卷數 | |
| 是否具有審稿制度 | 是 |
| 發表期數 | |
| 期刊或學報出版地國別/地區 | NATUSA-美國 |
| 發表年份 | 2014 |
| 發表月份 | 12 |
| 發表形式 | 紙本及電子期刊 |
| 所屬計劃案 | 科技部計畫 |
| 可公開文檔 |
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| 可公開文檔 |
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| 可公開文檔 | |
| 附件 |
JEI-14027(Gcuts).pdf
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[摘要] :
The normalized cut (Ncut) method is a popular method for segmenting images and videos. The Ncut
method segments an image into two disjoint regions, each segmented by the same method. After the Ncut
method has been recursively applied to an image, its final segmented image is obtained. The main drawback
of the Ncut method is that a user cannot easily determine the stop criteria because users have no idea about the
number of regions in an image. This work proposes the genetic cut (Gcut) algorithm to resolve this shortcoming.
Users do need not to specify thresholds in the Gcut algorithm, which automatically segments an image into the
proper number of regions. Also, the neighbor-merging (NM) algorithm is proposed for preprocessing the images
and improves the performance of the Gcut algorithm. Thus, the proposed Gcut method combines the NM and
Gcut algorithms. Furthermore, a heuristic method is proposed to identify a good segment for the Gcut method. In
all experiments, the proposed Gcut method outperforms traditional Ncut methods.
[英文摘要] :
The normalized cut (Ncut) method is a popular method for segmenting images and videos. The Ncut
method segments an image into two disjoint regions, each segmented by the same method. After the Ncut
method has been recursively applied to an image, its final segmented image is obtained. The main drawback
of the Ncut method is that a user cannot easily determine the stop criteria because users have no idea about the
number of regions in an image. This work proposes the genetic cut (Gcut) algorithm to resolve this shortcoming.
Users do need not to specify thresholds in the Gcut algorithm, which automatically segments an image into the
proper number of regions. Also, the neighbor-merging (NM) algorithm is proposed for preprocessing the images
and improves the performance of the Gcut algorithm. Thus, the proposed Gcut method combines the NM and
Gcut algorithms. Furthermore, a heuristic method is proposed to identify a good segment for the Gcut method. In
all experiments, the proposed Gcut method outperforms traditional Ncut methods.
