1. LTGH: A Dynamic Texture Feature for Working Condition Recognition in the Froth Flotation
- Author
-
Zhaohui Tang, Yongfang Xie, Hu Zhang, Luo Jin, and Fan Ying
- Subjects
Computer science ,business.industry ,Local binary patterns ,020208 electrical & electronic engineering ,Feature extraction ,Pattern recognition ,02 engineering and technology ,Texture (geology) ,Feature (computer vision) ,Robustness (computer science) ,Histogram ,0202 electrical engineering, electronic engineering, information engineering ,Artificial intelligence ,Electrical and Electronic Engineering ,Froth flotation ,business ,Instrumentation ,Rotation (mathematics) - Abstract
Texture feature of the froth image is widely used in the working condition recognition of froth flotation. However, due to the complexity of the froth image, the current texture features vary greatly and are difficult to identify the work condition accurately. Therefore, we propose a dynamic texture feature named LBP on the TOP and GLCM Histograms (LTGH) which integrates the local binary patterns (LBPs) and gray-level co-occurrence matrix (GLCM) histograms on the three orthogonal planes (TOP). First, we use the rotation invariant LBPs to enhance rotation invariance and illumination robustness. Then, we implement the TOP on the enhanced texture feature map to generate the multiple dimensional enhanced feature maps. After that, we calculate the GLCM and supplementary features (SFs) on the multiple dimensional enhanced feature map. Finally, we integrate the histogram of the GLCM and SFs to discriminate the texture feature. The LTGH feature considers the froth structures both in the macrolevel and microlevel and captures the temporal information between the froth images. Experiments have demonstrated the effectiveness and stability of the proposed texture feature for work condition recognition in froth flotation. Compared with other traditional texture features, the accuracy of the LTGH feature has been increased by at least 7.76%.
- Published
- 2021