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We compared three commonly used classifiers linear discriminant analysis (LDA) [42], random decision forests (RF) [43] (150 trees), and a SVM [37] (C-SVM, linear kernel with c=1) using three feature representations: PCA, one based on histogram of oriented gradients (HOG) [44, 45], and another based on local binary pattern (LBP) [46, 47] features.
Huang et al. [13] propose method based on local binary pattern (LBP).
In this work, we present a novel hybrid fingerprint matcher system based on local binary patterns.
Feng et al. [19] reported an accuracy of 93.8% based on local binary pattern histograms and linear programming techniques.
Addressing this issue, this paper proposes a new approach for fault diagnosis of induction motors utilizing two-dimensional texture analysis based on local binary patterns (LBPs).
Although texture analysis based on local binary patterns could be performed for different values of P and R, to ensure minimum computational complexity and lowest number of feature descriptors, we kept our analysis limited to P = 8 and R = 1.
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A theoretical model of pressure swing adsorption (PSA) processes that is based on local, linear equilibrium of a binary gas mixture with an adsorbent was experimentally tested under conditions supportive of the inherent assumptions and constraints.
Information about the geometrical appearance of underlying distributions was used to construct secondary binary masks based on local statistical and morphological operators.
This paper presents a new rolling bearing fault diagnosis method based on local mean decomposition (LMD), improved multiscale fuzzy entropy (IMFE), Laplacian score (LS) and improved support vector machine based binary tree (ISVM-BT).
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