Abstract Machine vision is widely used in the monitoring of froth flotation plants as a means
to assist control operators on the plant. While these systems have a mature ability to analyse …

Computer vision systems designed for flotation froth image analysis are well established in
industry, where their ability to measure froth flow velocities and stability are used to control …

In recent years, the use of machine vision systems for monitoring and control of the flotation
plants has significantly increased. The classification of froth images is a critical step in …

Bubble size and froth velocity are the most important froth characteristics used for evaluating
and controlling flotation systems. Bubble size is often measured using watershed and edge …

Deep learning constitutes a significant recent advance in machine learning and has been
particularly successful in applications related to image processing, where it can already …

Convolutional neural networks provide a state-of-the-art approach to the development of
froth image sensors. In this study, it is shown that a pretrained neural network architecture …

The control of the froth flotation process in the mineral industry is a challenging task due to
its multiple impacting parameters. Accurate and convenient examination of the concentrate …

In froth flotation monitoring, machine-vision-based soft sensors provide stable and reliable
online estimations for the concentrate grade, which is difficult to be measured online owing …

Image-based soft sensors are of interest in process industries due to their cost-effective and
non-intrusive properties. Unlike most multivariate inputs, images are highly dimensional …

Process condition recognition is an effective way to improve the froth process performance.
In previous condition recognition algorithms based on machine vision in flotation process …