Clustering-driven watershed adaptive segmentation of bubble image |
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Authors: | Kai-jun Zhou Chun-hua Yang Wei-hua Gui and Can-hui Xu |
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Affiliation: | (1) Lenox Institute of Water Technology, Lenox, MA, USA;(2) Krofta Engineering Corporation, Lenox, MA, USA;(3) University of Toledo, Toledo, OH, USA; |
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Abstract: | 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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Keywords: | flotation froth image adaptive segmentation pattern spectrum morphological feature |
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