Classification of Mineral Foam Flotation Conditions Based on Multi-Modality Image Fusion

Abstract

Accurate and rapid identification of mineral foam flotation states can increase mineral utilization and reduce the consumption of reagents. The traditional flotation process concentrates on extracting foam features from a single-modality foam image, and the accuracy is undesirable once problems such as insufficient image clarity or poor foam boundaries are encountered. In this work, a classification method based on multi-modality image fusion and CNN-PCA-SVM is proposed for work condition recognition of visible and infrared gray foam images. Specifically, the visible and infrared gray images are fused in the non-subsampled shearlet transform (NSST) domain using the parameter adaptive pulse coupled neural network (PAPCNN) method and the image quality detection method for high and low frequencies, respectively. The convolution neural network (CNN) is used as a trainable feature extractor to process the fused foam images, the principal component analysis (PCA) reduces feature data, and the support vector machine (SVM) is used as a recognizer to classify the foam flotation condition. After experiments, this model can fuse the foam images and recognize the flotation condition classification with high accuracy.