Improved image enhancement method for flotation froth image based on parameter extraction

Froth image could strongly indicate the production status in mineral flotation process.Considering low contrast and sensitivity to noises and illumination of froth images in flotation cells,an improved image enhancement algorithm based on nonsubsampled contourlet transform (NSCT) and multiscale Retinex algorithm has been proposed.Nonsubsampled contourlet transform was firstly adopted to decompose the flotation froth images,ensure signals invariance and avoid the blurring edge.Secondly,a multiscale Retinex algorithm was used to enhance the lower frequency image and improve the brightness uniformity.Adaptive classification method based on Bayes atrophy threshold was proposed to eliminate noise,preserve strong edges,and enhance weak edges of band-pass sub-band images.Experiment shows that the proposed method could enhance the edge,contour,details and curb noise,and improve visual effects.Under-segmentation caused by noise and blurring edge has been solved,which lays a foundation for extracting foamy morphological flotation froth and analyzing grade.

A froth velocity measurement method based on improved U-Net++semantic segmentation in flotation process

During flotation,the features of the froth image are highly correlated with the concentrate grade and the corresponding working conditions.The static features such as color and size of the bubbles and the dynamic features such as velocity have obvious differences between different working conditions.The extraction of these features is typically relied on the outcomes of image segmentation at the froth edge,making the segmentation of froth image the basis for studying its visual information.Meanwhile,the absence of scientifically reliable training data with label and the necessity to manually construct dataset and label make the study difficult in the mineral flotation.To solve this problem,this paper constructs a tungsten concentrate froth image dataset,and proposes a data augmentation network based on Conditional Generative Adversarial Nets(cGAN)and a U-Net++-based edge segmentation network.The performance of this algorithm is also evaluated and contrasted with other algorithms in this paper.On the results of semantic segmentation,a phase-correlationbased velocity extraction method is finally suggested.

Clustering-driven watershed adaptive segmentation of bubble image

In order to extract froth morphological feature,a bubble image adaptive segmentation method was proposed.Considering the image's low contrast and weak froth edges,froth image was coarsely segmented by using fuzzy c means(FCM) algorithm. Through the attributes of size and shape pattern spectrum,the optimal morphological structuring element was determined.According to the optimal parameters,some image noises were removed with an improved area opening and closing by reconstruction operation,which consist of image regional markers,and the bubbles were finely separated from each other by watershed transform.The experimental results show that the structural element can be determined adaptively by shape and size pattern spectrum,and the froth image is segmented accurately.Compared with other froth image segmentation method,the proposed method achieves much high accuracy,based on which,the bubble size and shape features are extracted effectively.

Fault detection in flotation processes based on deep learning and support vector machine

Effective fault detection techniques can help flotation plant reduce reagents consumption,increase mineral recovery,and reduce labor intensity.Traditional,online fault detection methods during flotation processes have concentrated on extracting a specific froth feature for segmentation,like color,shape,size and texture,always leading to undesirable accuracy and efficiency since the same segmentation algorithm could not be applied to every case.In this work,a new integrated method based on convolution neural network(CNN)combined with transfer learning approach and support vector machine(SVM)is proposed to automatically recognize the flotation condition.To be more specific,CNN function as a trainable feature extractor to process the froth images and SVM is used as a recognizer to implement fault detection.As compared with the existed recognition methods,it turns out that the CNN-SVM model can automatically retrieve features from the raw froth images and perform fault detection with high accuracy.Hence,a CNN-SVM based,real-time flotation monitoring system is proposed for application in an antimony flotation plant in China.