Flotation Bubble Delineation Based on Harris Corner Detection and Local Gray Value Minima

Weixing Wang,Liangqin Chen

doi:10.3390/min5020142

Weixing Wang, Liangqin Chen

Open Access

https://doi.org/10.3390/min5020142

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Journal: Minerals	Publication Date: Mar 30, 2015
Citations: 27	License type: CC BY 4.0

Affiliation: Fuzhou University, KTH Royal Institute of Technology

Abstract

Froth image segmentation is an important and basic part in an online froth monitoring system in mineral processing. The fast and accurate bubble delineation in a froth image is significant for the subsequent froth surface characterization. This paper proposes a froth image segmentation method combining image classification and image segmentation. In the method, an improved Harris corner detection algorithm is applied to classify froth images first. Then, for each class, the images are segmented by automatically choosing the corresponding parameters for identifying bubble edge points through extracting the local gray value minima. Finally, on the basis of the edge points, the bubbles are delineated by using a number of post-processing functions. Compared with the widely used Watershed algorithm and others for a number of lead zinc froth images in a flotation plant, the new method (algorithm) can alleviate the over-segmentation problem effectively. The experimental results show that the new method can produce good bubble delineation results automatically. In addition, its processing speed can also meet the online measurement requirements.

Highlights

Froth flotation is a selective separation process that is widely used in mineral processing to extract valuable minerals
(3) Taking advantage of the improved local gray value minima detection method to identify the minima point for each pixel to generate the binary edge image, where a 5 × 5 template is used for the images of large bubbles, and a 3 × 3 template for the images of non-large bubbles
This paper proposes a froth delineation method that is the combination of image classification and image segmentation

Summary

Introduction

Froth flotation is a selective separation process that is widely used in mineral processing to extract valuable minerals. Froth is a three-phase structure comprising air bubbles, solids and water [1]. The control of flotation process depends heavily on various experiences of human operators by looking at the appearance of the froth [2]. The performance depends on the operator’s experience and is limited by the absence of physical, quantitative methods for measurement and characterization of the froth [3]. Methods based on machine vision and image processing have been developed for observation and analysis of froth images, including the application for extraction of bubble size, shape and other physical features [4,5,6]

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