Flotation Bubble Delineation Based on Harris Corner Detection and Local Gray Value Minima
AbstractFroth 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. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Wang, W.; Chen, L. Flotation Bubble Delineation Based on Harris Corner Detection and Local Gray Value Minima. Minerals 2015, 5, 142-163.
Wang W, Chen L. Flotation Bubble Delineation Based on Harris Corner Detection and Local Gray Value Minima. Minerals. 2015; 5(2):142-163.Chicago/Turabian Style
Wang, Weixing; Chen, Liangqin. 2015. "Flotation Bubble Delineation Based on Harris Corner Detection and Local Gray Value Minima." Minerals 5, no. 2: 142-163.