A Similarity-Based Burst Bubble Recognition Using Weighted Normalized Cross Correlation and Chamfer Distance

The burst bubble rate has been strongly linked to froth stability, and thus, it is always used for performance prediction or modeling in the froth flotation. Due to different bubble motions and intensity changes as the bubbles move, the current burst bubble recognition methods are ineffective. Therefore, in this article, a similarity-based method for burst bubble recognition is proposed. The proposed method uses the local motion correction to deal with the different motion cases, and it selects the chamfer distance and the weighted normalized cross correlation as the similarity to decrease the influence of the intensity changes by the convex shape. Furthermore, the weighted normalized cross correlation is flexibly integrated with the template mask matching and the partial template matching. Extensive experiments have validated the effectiveness and robustness of the proposed method, where the precision and ${F}_{1}$-score have been increased by at least 7.41% and 4.36%, respectively, compared with the current methods.