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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References
KAARTINEN J, HATONEN J. Machine vision based control of zinc Flotation—A case study [J]. Control Engineering Practice, 2006, 14(12): 1455–1466.
YANG Chun-hua, YANG Jin-ying, MU Xue-min, ZHOU Kai-jun, GUI Wei-hua. A segmentation method based on clustering pre-segmentation and high-low scale distance reconstruction for colour froth image [J]. Journal of Electronics & Information Technology, 2008, 30(6): 1286–1290. (in Chinese)
YANG Chun-hua, XU Can-hui, GUI Wei-hua, ZHOU Kai-jun. Application of highlight removal and multivariate image analysis to color measurement of flotation bubble images [J]. International Journal of Imaging Systems and Technology, 2009, 19(4): 316–322.
BONIFAZI G, SERRANTI S, VOLPE F, ZUCO R. Characterisation of flotation froth colour and structure by machine vision [J]. Computers & Geosciences, 2001, 27(9): 1111–1117.
LIN Xiao-zu, GU Ying-ying, ZHAO Guo-qing. Feature extraction based on image segmentation of coal flotation froth [J]. Journal of the China Coal Society, 2007, 32(3): 304–308. (in Chinese)
WANG W X, BERGHOLM F, YANG B. Froth delineation based on image classification [J]. Mineral Engineering, 2003, 16(11): 1183–1192.
YANG Chun-hua, XU Can-hui, MU Xue-min, ZHOU Kai-jun. Bubble size estimation using interfacial morphological information for mineral flotation process monitoring [J]. Transactions of Nonferrous Metals Society of China, 2009, 19(3): 694–699.
LIN B, RECKE B, KNUDSEN J K H, JORGENSEN S B. Bubble size estimation for flotation processes [J]. Minerals Engineering, 2008, 21(7): 539–548.
MEIJSTER A, WILKINSON M H F. A comparison of algorithms for connected set openings and closings [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(4): 484–494.
WILKINSON M H F, WESTENBERG M A. Shape preserving filament enhancement filtering [C]// Proceedings of the 4th International Conference on Medical Image Computing and Computer Aided Intervention. Utrecht: IEEE Press, 2001: 770–777.
BRAGA-NETO U, GOUTSIAS J. Object-based image analysis using multiscale connectivity [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(6): 892–907.
HAMARNEH G, LI X X. Watershed segmentation using prior shape and appearance knowledge [J]. Image and Vision Computing, 2009, 27(1/2): 59–68.
ZHOU Xian-cheng, SHEN Qun-tai, LIU Li-mei. New two-dimensional fuzzy C-means clustering algorithm for image segmentation [J]. Journal of Central South University of Technology, 2008, 15(6): 882–887.
URBACH E R, ROERDINK J B T M, WILKINSON M H F. Connected shape-size pattern spectra for rotation and scale invariant classification of gray scale images [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(2): 272–285.
SERRA J. A lattice approach to image segmentation [J]. Journal of Mathematical Imaging and Vision, 2006, 24(1): 83–130.
SALEMBIER P, SERRA J. Flat zones filtering connected operators and filters by reconstruction [J]. IEEE Transactions on Image Processing, 1995, 4(8): 1153–1160.
NG H F. Automatic thresholding for defect detection [J]. Pattern Recognition Letters, 2006, 27(14): 1644–1649.
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Foundation item: Projects(60634020, 60874069) supported by the National Natural Science Foundation of China; Project(2009AA04Z137) supported by the National High-Tech Research and Development Program of China
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Zhou, Kj., Yang, Ch., Gui, Wh. et al. Clustering-driven watershed adaptive segmentation of bubble image. J. Cent. South Univ. Technol. 17, 1049–1057 (2010). https://doi.org/10.1007/s11771-010-0597-y
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DOI: https://doi.org/10.1007/s11771-010-0597-y