Knowledge from Markers in Watershed Segmentation
Knowledge from Markers in Watershed Segmentation
复制标题
分水岭分割中标记的知识
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
S. Lefèvre
中科院分区：
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作者：
S. Lefèvre
由于图像分割在图像分析中的广泛应用，图像分割问题得到了广泛的研究。然而，仍然不存在任何自动分割程序能够准确地处理任何类型的图像。因此，半自动分割方法可以被视为解决分割问题的适当替代方案。在这些方法中，基于标记的分水岭方法已经成功地应用于各个领域。在该算法中，用户可以定位标记，标记仅用作待分割区域的初始起始位置。我们建议通过监督像素分类的标记的内容上的分割过程的基础，从而导致基于知识的分水岭分割的知识是建立在从标记。我们的贡献已经通过一些比较测试与一些国家的最先进的方法在著名的伯克利分割数据集进行了评估。
Due to its broad impact in many image analysis applications, the problem of image segmentation has been widely studied. However, there still does not exist any automatic segmentation procedure able to deal accurately with any kind of image. Thus semi-automatic segmentation methods may be seen as an appropriate alternative to solve the segmentation problem. Among these methods, the marker-based watershed has been successfully involved in various domains. In this algorithm, the user may locate the markers, which are used only as the initial starting positions of the regions to be segmented. We propose to base the segmentation process also on the contents of the markers through a supervised pixel classification, thus resulting in a knowledge-based watershed segmentation where the knowledge is built from the markers. Our contribution has been evaluated through some comparative tests with some state-of-the-art methods on the well-known Berkeley Segmentation Dataset.