An Improved Python-Based Image Processing Algorithm for Flotation Foam Analysis

For industrial flotation foam image processing, accurate bubble size measurement and feature extraction are very important to optimize the flotation process and to improve the recovery of mineral resources. This paper presents an improved algorithm to investigate mineral flotation foam image segmentation for mineral processing. Several libraries implemented for the Python programming language are used for image enhancement and compensation, quantitative analysis of factors influencing the image segmentation accuracy, and suggestions for improvement of the flotation foam image processing. The bubble characteristics-size and morphology-and the influence of the flotation conditions on the flotation foam image are analyzed. A Python implementation of the Retinex image compensation method-region-adaptive and multiscale-is proposed to address known issues of uneven illumination and shadows affecting flotation foam images, thereby improving brightness uniformity. Finally, an improved version of the watershed segmentation algorithm included in the Python Open Source Computer Vision library is used for segmentation analysis. The accuracy of the flotation foam image segmentation is 3.3% higher than for the standard watershed algorithm and the segmentation time is 9.9% shorter.

Automated summary

This paper presents an improved algorithm for mineral flotation foam image segmentation in order to optimize the flotation process and improve the recovery of mineral resources. Python libraries are used for image enhancement and compensation, quantitative analysis of factors affecting image segmentation accuracy, and suggestions for improvement. The study analyzes the characteristics of the bubbles and the influence of flotation conditions on the foam image. An improved version of the watershed segmentation algorithm is used for segmentation analysis, resulting in higher accuracy and shorter segmentation time compared to the standard algorithm.