Machine Vision Based Production Condition Classification and Recognition for Mineral Flotation Process Monitoring

Abstract

A novel froth image analysis based production condition recognition method is presented to identify the froth phases under various production conditions. Gabor wavelet transformation is employed to froth image processing firstly due to the ability of Gabor functions in simulating the response of the simple cells in the visual cortex. Successively, the statistical distribution profiles based feature parameters of the Gabor filter responses rather than the conventional mean and variance are extracted to delineate the essential statistical information of the froth images. The amplitude and phase representations of the Gabor filter responses are both taken into account by empirical marginal and joint statistical modeling. At last, a simple learning vector quantization (LVQ) neural network model is used to learn an effective classifier to recognize the froth production conditions. The effectiveness of this method is validated by the real production data on industrial scale from a bauxite dressing plant.

Keywords:

• Foth flotation process
• froth image
• production condition classification and recognition
• Gabor wavelet transform
• marginal distribution
• joint distribution