Flotation froth image texture extraction method based on deterministic tourist walks

In the flotation process, the flotation froth texture is an indicator of the flotation state. To recognize the flotation state based on texture features accurately and to provide guidance for production operations, this paper proposes a method for flotation froth image texture extraction based on the deterministic tourist walks algorithm. First, a weighted graph model of a froth image is built using deterministic tourist walks. Next, the degree distribution and the unit intensity distribution of the weighted graph are extracted. The contrast of the node degree and the contrast of the node unit intensity are calculated as the texture feature indexes. The texture feature indexes are used for flotation production state classification and recognition. The experimental results demonstrate that the proposed method can extract froth image texture features accurately and provide effective guidance for flotation production.

[1]  Weihua Gui,et al.  Spatial‐temporal fusion for flotation froth image denoising based on BLS‐GSM method in curvelet domain , 2014 .

[2]  Gui Wei-hua,et al.  Machine-vision-based Online Measuring and Controlling Technologies for Mineral Flotation--A Review , 2013 .

[3]  W. Wang,et al.  Froth delineation based on image classification , 2003 .

[4]  D. Boyera,et al.  Modeling the searching behavior of social monkeys , 2004 .

[5]  Gui Wei-hua,et al.  A novel texture extraction and classification method for mineral froth images based on complex networks , 2014, Proceeding of the 11th World Congress on Intelligent Control and Automation.

[6]  Ruiyu Liang,et al.  A vision inspection system for the surface defects of strongly reflected metal based on multi-class SVM , 2011, Expert Syst. Appl..

[7]  Jan J. Cilliers,et al.  An image processing algorithm for measurement of flotation froth bubble size and shape distributions , 1997 .

[8]  Chris Aldrich,et al.  The interrelationship between surface froth characteristics and industrial flotation performance , 1996 .

[9]  J. Douglas Aspects and applications of the random walk , 1995 .

[10]  André Ricardo Backes,et al.  Texture analysis using graphs generated by deterministic partially self-avoiding walks , 2011, Pattern Recognit..

[11]  Germinal Cocho,et al.  Scale-free foraging by primates emerges from their interaction with a complex environment , 2006, Proceedings of the Royal Society B: Biological Sciences.

[12]  L. da F. Costa,et al.  Characterization of complex networks: A survey of measurements , 2005, cond-mat/0505185.

[13]  J. Kaartinena,et al.  Machine-vision-based control of zinc flotation — A case study , 2009 .

[14]  Nick J. Miles,et al.  The use of grey level measurement in predicting coal flotation performance , 1996 .

[15]  André Ricardo Backes,et al.  A complex network-based approach for boundary shape analysis , 2009, Pattern Recognit..

[16]  O. Kinouchi,et al.  Deterministic Walks in Random Networks: An Application to Thesaurus Graphs , 2001, cond-mat/0110217.

[17]  John F. MacGregor,et al.  Froth-based modeling and control of flotation processes , 2008 .

[18]  Jinping Liu,et al.  Color co-occurrence matrix based froth image texture extraction for mineral flotation , 2013 .

[19]  Luciano da Fontoura Costa Complex Networks, Simple Vision , 2004 .

[20]  Weihua Gui,et al.  Reagent dosages control based on bubble size characteristics for flotation process , 2016 .

[21]  Osame Kinouchi,et al.  Deterministic walks as an algorithm of pattern recognition. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[22]  Zhang Xuewu,et al.  A vision inspection system for the surface defects of strongly reflected metal based on multi-class SVM , 2011 .

[23]  K. E. Cole,et al.  Combining Positron Emission Particle Tracking and image analysis to interpret particle motion in froths , 2010 .

[24]  André Ricardo Backes,et al.  Texture analysis and classification: A complex network-based approach , 2013, Inf. Sci..