TCvBsISM: Texture Classification via B-Splines-Based Image Statistical Modeling

Liu, Jin Ping; He, Jiezhou; Zhang, Wuxia; Xu, Pengfei; Tang, Zhaohui

doi:10.1109/access.2018.2864754

Cited by 13 publications

(4 citation statements)

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“…This bauxite flotation circuit includes three basic subprocesses: rougher, cleaner (including cleaner I and cleaner II), and scavenger (including roughing scavenger and cleaning scavenger) [7,8,[44][45][46]. Wherein, the concentrate froth of the rougher sub-process is collected and pumped to the cleaner I for further processing to improve the cleaner grade.…”

Section: Case Study On a Real Flotation Processmentioning

confidence: 99%

STA-APSNFIS: STA-Optimized Adaptive Pre-Sparse Neuro-Fuzzy Inference System for Online Soft Sensor Modeling

Liu

Jiang

et al. 2020

IEEE Access

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In complex industrial processes (CIPs), due to technical and economic limitations, key performance indicators (KPIs), especially the chemical content-related KPIs, are often difficult to measure in real time, which hinders the propagation of advanced process control technologies. This paper presents a soft sensor-based online KPI inference scheme by a state transition algorithm (STA)-optimized adaptive pre-sparse neuro-fuzzy inference system model, called STA-APSNFIS. It introduces a pre-sparse neural network to the traditional adaptive neuro-fuzzy inference system (ANFIS) model to establish an adaptive pre-sparse neuro-fuzzy inference system (APSNFIS) model to alleviate the adverse effects of data redundancy and noise interference in the detectable process monitoring data, which can effectively reduce the complexity of neuro-fuzzy inference system (NFIS) and speed up its convergence. Successively, to avoid being trapped at a local optimum, the STA-based optimization algorithm is adopted to replace the traditional gradient-based optimization approach to achieve an optimal APSNFIS model. Extensive validation and comparative experiments on nonlinear numeric simulation systems, benchmark Tenessee Eastman (TE) process and a real industrial bauxite flotation process demonstrated that the proposed STA-APSNFIS performed favorably against traditional ANFIS model as well as its variants, e.g., PSO-ANFIS, GA-ANFIS, and some other soft sensor-based KPI inference models.

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Section: Case Study On a Real Flotation Processmentioning

confidence: 99%

STA-APSNFIS: STA-Optimized Adaptive Pre-Sparse Neuro-Fuzzy Inference System for Online Soft Sensor Modeling

Liu

Jiang

et al. 2020

IEEE Access

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“…Texture description is one of the main and active fields of research in computer vision [ 1 ] and it has a high impact in several research areas connected to image processing and pattern recognition. Texture description is a challenging task that deals with several open problems, e.g., highly discriminate inter-class textures while achieving robustness to intra-class variations.…”

Section: Introductionmentioning

confidence: 99%

Boosting Texture-Based Classification by Describing Statistical Information of Gray-Levels Differences

García-Olalla

Fernández-Robles

Alegre

et al. 2019

Sensors

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This paper presents a new texture descriptor booster, Complete Local Oriented Statistical Information Booster (CLOSIB), based on statistical information of the image. Our proposal uses the statistical information of the texture provided by the image gray-levels differences to increase the discriminative capability of Local Binary Patterns (LBP)-based and other texture descriptors. We demonstrated that Half-CLOSIB and M-CLOSIB versions are more efficient and precise than the general one. H-CLOSIB may eliminate redundant statistical information and the multi-scale version, M-CLOSIB, is more robust. We evaluated our method using four datasets: KTH TIPS (2-a) for material recognition, UIUC and USPTex for general texture recognition and JAFFE for face recognition. The results show that when we combine CLOSIB with well-known LBP-based descriptors, the hit rate increases in all the cases, introducing in this way the idea that CLOSIB can be used to enhance the description of texture in a significant number of situations. Additionally, a comparison with recent algorithms demonstrates that a combination of LBP methods with CLOSIB variants obtains comparable results to those of the state-of-the-art.

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“…Considerable efforts have devoted to the statistical distribution modeling-(SDM-) based methods to the LSD characterization to learn a fuller representation of the distribution models based on the random field constraint, especially integrated with the prevalent multichannel and multiscale image analysis, such as dyadic Wavelet transform (DWT) and Gabor Wavelet transform (GWT) [30]. Many useful statistical models are introduced to approximate the LSDs of TPs in the filter bank response domains [31,32].…”

Section: Introductionmentioning

confidence: 99%

Characterization of Complex Image Spatial Structures Based on Symmetrical Weibull Distribution Model for Texture Pattern Classification

Liu

Tang

et al. 2018

Complexity

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Texture pattern classification has long been an essential issue in computer vision (CV). However, texture is a kind of perceptual concept of human beings in scene observation or content understanding, which cannot be defined or described clearly in CV. Visually, the visual appearance of the complex spatial structure (CSS) of texture pattern (TP) generally depends on the random organization (or layout) of local homogeneous fragments (LHFs) in the imaged surface. Hence, it is essential to investigate the latent statistical distribution (LSD) behavior of LHFs for distinctive CSS feature characterization to achieve good classification performance. This work presents an image statistical modeling-based TP identification (ISM-TPI) method. It firstly makes a theoretical explanation of the Weibull distribution (WD) behavior of the LHFs of the imaged surface in the imaging process based on the sequential fragmentation theory (SFT), which consequently derives a symmetrical WD model (SWDM) to characterize the LSD of the TP’s SS. Multidirectional and multiscale TP features are then characterized by the SWDM parameters based on the oriented differential operators; in other words, texture images are convolved with multiscale and multidirectional Gaussian derivative filters (GDFs), including the steerable isotropic GDFs (SIGDFs) and the oriented anisotropic GDFs (OAGDFs), for the omnidirectional and multiscale SS detail exhibition with low computational complexity. Finally, SWDM-based TP feature parameters, demonstrated to be directly related to the human vision perception system with significant physical perception meaning, are extracted and used to TP classification with a partial least squares-discriminant analysis- (PLS-DA-) based classifier. The effectiveness of the proposed ISM-TPI method is verified by extensive experiments on three texture image databases. The classification results demonstrate the superiority of the proposed methods over several state-of-the-art TP classification methods.

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TCvBsISM: Texture Classification via B-Splines-Based Image Statistical Modeling

Cited by 13 publications

References 50 publications

STA-APSNFIS: STA-Optimized Adaptive Pre-Sparse Neuro-Fuzzy Inference System for Online Soft Sensor Modeling

STA-APSNFIS: STA-Optimized Adaptive Pre-Sparse Neuro-Fuzzy Inference System for Online Soft Sensor Modeling

Boosting Texture-Based Classification by Describing Statistical Information of Gray-Levels Differences

Characterization of Complex Image Spatial Structures Based on Symmetrical Weibull Distribution Model for Texture Pattern Classification

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