Froth Image Acquisition and Enhancement on Optical Correction and Retinex Compensation

To well monitor and optimize the flotation production, a computer vision and image analysis system is used. In such a system, the first important step is to acquire the froth surface images in high quality. Froth imaging quality is hard to control, and the industrial field noise, froth 3D properties, complex textures, and mixed colors can also cause the flotation image to be difficult to segment and process. To acquire high quality images, a new system for image acquisition of the lead flotation is studied. The system constructs the free-form surface lens based on the non-imaging optics theory, which can improve the optical efficiency of the lens and the uniformity of light sources, and can reduce flare effects. For the compensation, an improved MSR (Multi-Scale Retinex) adaptive image algorithm is proposed to increase the brightness and intensity contrast for small bubbles, and to enhance texture details and froth weak edges by analyzing the Retinex output characteristics of the shaded area and improving the gain function. Under the condition of the optimal parameters, the image acquisition system can obtain uniform illumination and reduce different noises. Experiments show that the new froth image acquisition system increases Signal/Noise by 14%, contrast by 21%, and image segmentation accuracy by 26% in an image.

[1]  Aldo Cipriano,et al.  A REAL TIME VISUAL SENSOR FOR SUPERVISION OF FLOTATION CELLS , 1998 .

[2]  Silvia Serranti,et al.  Characterization of the flotation froth structure and color by machine vision (ChaCo) , 2000 .

[3]  Zeng Rong Study of Edge Detection Methods on Flotation Froth Image , 2002 .

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

[5]  Wang Yong The gray run length and its statistical texture features of coal flotation froth image , 2006 .

[6]  Liu Xu,et al.  Freeform surface lens design for uniform?illumination , 2008 .

[7]  Deng Guoqiang Non-Imaging Optics and Its Application in Solid State Lighting , 2008 .

[8]  Weihua Gui,et al.  Application of highlight removal and multivariate image analysis to color measurement of flotation bubble images , 2009, Int. J. Imaging Syst. Technol..

[9]  Chris Aldrich,et al.  Estimation of platinum flotation grades from froth image data , 2011 .

[10]  Weixing Wang Colony image acquisition system and segmentation algorithms , 2011 .

[11]  Ko Nishino,et al.  Bayesian Defogging , 2012, International Journal of Computer Vision.

[12]  Jean-Philippe Tarel,et al.  Vision Enhancement in Homogeneous and Heterogeneous Fog , 2012, IEEE Intelligent Transportation Systems Magazine.

[13]  Weixing Wang,et al.  Bubble delineation on valley edge detection and region merge , 2013 .

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

[15]  Yang Chunhua,et al.  Froth homogeneity analysis using rotate classification fuzzy texture spectrum for mineral flotation process monitoring , 2013 .

[16]  Chris Aldrich,et al.  Automatic estimation of bubble size distributions in flotation froths by use of a mean shift algorithm and watershed transforms , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[17]  Zhang Xin,et al.  Image enhancement on fractional differential for road traffic and aerial images under bad weather and complicated situations , 2014 .

[18]  Tian Liping,et al.  Fuzzy and Touching Cell Extraction on Modified Graph Minimize Spanning Tree and Skeleton Distance Mapping Histogram , 2014 .

[19]  Vivian Vimarlund,et al.  Applications of terrestrial laser scanning for tunnels : a review , 2014 .

[20]  Weixing Wang,et al.  Retinex Algorithm on Changing Scales for Haze Removal with Depth Map , 2014 .

[21]  Chen Xiao,et al.  Complex networks-based texture extraction and classification method for mineral flotation froth images , 2015 .

[22]  E. C. Cilek,et al.  Effect of nanoparticles on froth stability and bubble size distribution in flotation , 2015 .

[23]  Weixing Wang,et al.  Flotation Bubble Delineation Based on Harris Corner Detection and Local Gray Value Minima , 2015 .

[24]  W. Nyabeze,et al.  Adsorption of copper sulphate on PGM-bearing ores and its influence on froth stability and flotation kinetics , 2016 .

[25]  Nan Yang,et al.  A review of road extraction from remote sensing images , 2016 .

[26]  Weihua Gui,et al.  Recognition of flooding and sinking conditions in flotation process using soft measurement of froth surface level and QTA , 2017 .

[27]  Mohammad Hamiruce Marhaban,et al.  An image segmentation algorithm for measurement of flotation froth bubble size distributions , 2017 .

[28]  May Namutebi,et al.  Exploratory study on bitumen content determination for foamed bitumen mixes based on porosity and indirect tensile strength , 2017 .