Method for extracting froth velocity of coal slime flotation based on image feature matching

Abstract

The local static characteristics of coal slime flotation foam image are similar. The dynamic characteristics of flotation foam image are needed for judging some complex working conditions. The existing extraction method for the dynamic features of the froth velocity of coal slime flotation has the problems of insufficient accuracy, real-time performance and stability. In order to solve the above problems, a feature extraction method of froth velocity in coal slime flotation based on image feature matching is proposed. Firstly, the contrast limited adaptive histogram equalization (CLAHE) and block-matching and 3D filtering(BM3D) are used to preprocess the flotation froth image to improve the quality of the image and highlight the edge details of the image. Secondly, the accelerated-KAZE (AKAZE) algorithm of accelerated features in nonlinear scale space is used to detect the feature points of flotation froth features. Thirdly, on the basis of rough matching of froth image features by brute-force matching (BF), a grid-based motion statistics (GMS) algorithm is used to quickly and reliably distinguish correct and wrong feature matching. Finally, the method calculates the slime foam velocity according to the feature matching results. The foam velocity is taken as the measured value. The Kalman motion estimation method is used to iteratively modify the measured values to obtain more stable foam velocity characteristics of coal slime flotation. The experimental results show the following points. ① The AKAZE-GMS algorithm can solve the problem of feature point clustering well and keep more feature points as much as possible. This is because the preprocessed image is less affected by noise, has better contrast, and has more prominent edge features. ② Compared with SIFT (scale-invariant feature transform), SURF (speeded up robust features) and AKAZE, the AKAZE-GMS algorithm has a more uniform distribution of matching pairs, retains more correct matching pairs. The method achieves a matching accuracy of 99.99%. The running time is only 3.73 s. ③ The measured value of froth velocity directly calculated from the feature matching results fluctuates greatly. The velocity estimated value of the measured value corrected by Kalman motion estimation is more stable, which is more consistent with the real working condition.

Keywords

• coal slime flotation
• flotation froth velocity
• dynamic features of flotation froth
• image feature matching
• fast robust feature matching filtering
• kalman motion estimation