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Clustering-driven watershed adaptive segmentation of bubble image

期刊名称：Journal of Central South University of Technology
时间：0
页码：1049-1057
语言：英文
分类：TP391.41[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术] TN919.81[电子电信—通信与信息系统;电子电信—信息与通信工程]
作者机构：[1]School of Information Science and Engineering, Central South University, Changsha 410083, China, [2]School of Computer and Electronic Engineering, Hunan University of Commerce, Changsha 401201, China
相关基金：Projects（60634020, 60874069） supported by the National Natural Science Foundation of China： Project（2009AA04Z137） supported by the National High-Tcch Research and Development Program of China
相关项目：数据驱动的多相交互冶金过程能耗优化方法研究及应用

作者： Yang Chunhua|Gui Weihua|Zhou Kaijun|

关键词：图像分割方法, 自适应分割, 分水岭变换, 微气泡, 泡沫图像, 驱动, 群集, 形状特征, flotation, froth image, adaptive segmentation, pattern spectrum, morphological feature

中文摘要：

以便提取泡词法特征，一幅水泡图象适应分割方法被建议。认为图象是低对比和弱泡边，泡图象被使用模糊 c 工具(FCM ) 粗糙地分割算法。通过尺寸和形状模式光谱的属性，最佳的词法组织元素是坚定的。根据最佳的参数，一些图象噪音被移开，一个改进区域由重建操作开并且关门，它由图象组成地区性的标记，和水泡被分水岭细微地与对方分开变换。试验性的结果证明结构的元素能由形状和尺寸模式光谱适应地是坚定的，并且泡图象精确地被分割。与另外的泡图象分割方法相比，建议方法完成许多高精确性，基于哪个，水泡尺寸和形状特征有效地被提取。

英文摘要：

In order to extract froth morphological feature, a bubble image adaptive segmentation method was proposed. Considering the image＇s low contrast and weak froth edges, froth image was coarsely segmented by using fuzzy c means （FCM） algorithm. Through the attributes of size and shape pattern spectrum, the optimal morphological structuring element was determined. According to the optimal parameters, some image noises were removed with an improved area opening and closing by reconstruction operation, which consist of image regional markers, and the bubbles were finely separated from each other by watershed transform. The experimental results show that the structural element ,can be determined adaptively by shape and size pattern spectrum, and the froth image is segmented accurately. Compared with other froth image segmentation method, the proposed method achieves much high accuracy, based on which, the bubble size and shape features are extracted effectively.

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