Journal ArticleDOI
LTGH: A Dynamic Texture Feature for Working Condition Recognition in the Froth Flotation
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TLDR
Li et al. as discussed by the authors proposed 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).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%.read more
Citations
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Journal ArticleDOI
RPI-SURF: A Feature Descriptor for Bubble Velocity Measurement in Froth Flotation With Relative Position Information
TL;DR: Wang et al. as discussed by the authors proposed a new feature descriptor named relative position information (RPI)-SURF that integrates the local intensity character (LIC) and the RPI from the nearest reference point.
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FS-DeblurGAN: a spatiotemporal deblurring method for zinc froth flotation
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FS-DeblurGAN: a spatiotemporal deblurring method for zinc froth flotation
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An Improved Python-Based Image Processing Algorithm for Flotation Foam Analysis
TL;DR: In this paper , a Python implementation of the Retinex image compensation method is proposed to address known issues of uneven illumination and shadows affecting flotation foam images, thereby improving brightness uniformity.
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Grouped Time Series Networks for Grade Monitoring of Zinc Tailings With Multisource Features
TL;DR: Wang et al. as discussed by the authors proposed a grouped time series network (GTSN) with multisource features for grade monitoring of zinc tailings in a case study in a real zinc froth flotation process.
References
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Journal ArticleDOI
Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Journal ArticleDOI
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
TL;DR: A generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis.
Journal ArticleDOI
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
Guoying Zhao,Matti Pietikäinen +1 more
TL;DR: A novel approach for recognizing DTs is proposed and its simplifications and extensions to facial image analysis are also considered and both the VLBP and LBP-TOP clearly outperformed the earlier approaches.
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Dynamic Textures
TL;DR: A characterization of dynamic textures that poses the problems of modeling, learning, recognizing and synthesizing dynamic textures on a firm analytical footing and experimental evidence that, within the framework, even low-dimensional models can capture very complex visual phenomena is presented.
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Rotation invariant texture classification using LBP variance (LBPV) with global matching
TL;DR: The experimental results on representative databases show that the proposed LBPV operator and global matching scheme can achieve significant improvement, sometimes more than 10% in terms of classification accuracy, over traditional locally rotation invariant LBP method.