A New Feature Extraction Technique Based on 1D Local Binary Pattern for Gear Fault Detection

Abdul, Zrar Kh.; Al-Talabani, Abdulbasit K.; Abdulrahman, Ayub Othman

doi:10.1155/2016/8538165

Cited by 10 publications

(7 citation statements)

References 11 publications

(16 reference statements)

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“…The IAC region is distinguished by its shape and soft tissue structure, so features extracted using a mixture of HOG [27, 28], LBP [29, 30], and GLCM [31] are considered for classification. The HOG descriptor describes explicates the structure or the shape of an object.…”

Section: Methodsmentioning

confidence: 99%

Inferior alveolar nerve canal segmentation by local features based neural network model

Maheswari¹,

Banumathi²,

Ulaganathan³

et al. 2021

IET Image Processing

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The detection of Inferior Alveolar Nerve Canal (IAC) plays major and crucial role in dental surgical procedures to avoid damage to IAC during the course of treatment. Exact visualization and detection of IAC is necessary for precise surgery planning to prevent IAC damage. The proposed method comprises of three stages namely, novel edge enhancement, candidate classification and candidate pixel clustering to detect the IAC. For better visualization of IAC, initially the edges of dental OPG images are enhanced using a novel structural filter. Candidate regions are selected from the enhanced image by the proposed Multi Hidden Layer Extreme Learning Machine Artificial Neural Network (MELMANN) model driven by combined regional features such as Histogram of Gradients (HOG), Local Binary Pattern (LBP) and Gray Level Co‐occurrence Matrix (GLCM). Consequently the candidate region pixels are clustered by a Self‐Organising Map‐based Neural Network (SOM ‐ NNC) along with active contour method to detect the IAC completely. Experimental results show that this method effectively delineated the IAC with the Dice coefficient of 0.854 ± 0.05.Therefore, the proposed method has high potential in clearly visualizing IAC to avoid neurological sensory disorders in oral and maxillofacial surgery and implantology and it provides better prediagnostic approach to the surgeons.

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Section: Methodsmentioning

confidence: 99%

Inferior alveolar nerve canal segmentation by local features based neural network model

Maheswari¹,

Banumathi²,

Ulaganathan³

et al. 2021

IET Image Processing

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show abstract

“…Algorithm 2 ( 1 SSCE and 2 Dispersion Entropy [42][43][44]) in the supplementary materials section shows the pseudocode for the overall IMU feature extraction. [45] technique. It focuses on the vibration of the signal, and captures the descriptive information representing the relative changes in the IMU signal amplitudes.…”

Section: Imu-based Hybrid Feature Extractionmentioning

confidence: 99%

“…1D-LBP is a non-parametric statistical feature extraction [45] technique. It focuses on the vibration of the signal, and captures the descriptive information representing the relative changes in the IMU signal amplitudes.…”

Section: D Local Binary Patternmentioning

confidence: 99%

HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks

2021

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The daily life-log routines of elderly individuals are susceptible to numerous complications in their physical healthcare patterns. Some of these complications can cause injuries, followed by extensive and expensive recovery stages. It is important to identify physical healthcare patterns that can describe and convey the exact state of an individual’s physical health while they perform their daily life activities. In this paper, we propose a novel Sustainable Physical Healthcare Pattern Recognition (SPHR) approach using a hybrid features model that is capable of distinguishing multiple physical activities based on a multiple wearable sensors system. Initially, we acquired raw data from well-known datasets, i.e., mobile health and human gait databases comprised of multiple human activities. The proposed strategy includes data pre-processing, hybrid feature detection, and feature-to-feature fusion and reduction, followed by codebook generation and classification, which can recognize sustainable physical healthcare patterns. Feature-to-feature fusion unites the cues from all of the sensors, and Gaussian mixture models are used for the codebook generation. For the classification, we recommend deep belief networks with restricted Boltzmann machines for five hidden layers. Finally, the results are compared with state-of-the-art techniques in order to demonstrate significant improvements in accuracy for physical healthcare pattern recognition. The experiments show that the proposed architecture attained improved accuracy rates for both datasets, and that it represents a significant sustainable physical healthcare pattern recognition (SPHR) approach. The anticipated system has potential for use in human–machine interaction domains such as continuous movement recognition, pattern-based surveillance, mobility assistance, and robot control systems.

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“…1D LPB is statistically used as a nonparametric operator which defines the number of counts for each change in the inertial sensors signals that exceed the threshold. For each data sample, a binary code produced and found precise variations in the processed inertial signals [ 24 ]. In this algorithm, the middle sample is selected as a threshold value and the other values are compared against the particular threshold.…”

Section: Designed Framework For Wearable Harmentioning

confidence: 99%

Wearable Inertial Sensors for Daily Activity Analysis Based on Adam Optimization and the Maximum Entropy Markov Model

Tahir

Jalal

Kim

2020

Entropy

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Advancements in wearable sensors technologies provide prominent effects in the daily life activities of humans. These wearable sensors are gaining more awareness in healthcare for the elderly to ensure their independent living and to improve their comfort. In this paper, we present a human activity recognition model that acquires signal data from motion node sensors including inertial sensors, i.e., gyroscopes and accelerometers. First, the inertial data is processed via multiple filters such as Savitzky–Golay, median and hampel filters to examine lower/upper cutoff frequency behaviors. Second, it extracts a multifused model for statistical, wavelet and binary features to maximize the occurrence of optimal feature values. Then, adaptive moment estimation (Adam) and AdaDelta are introduced in a feature optimization phase to adopt learning rate patterns. These optimized patterns are further processed by the maximum entropy Markov model (MEMM) for empirical expectation and highest entropy, which measure signal variances for outperformed accuracy results. Our model was experimentally evaluated on University of Southern California Human Activity Dataset (USC-HAD) as a benchmark dataset and on an Intelligent Mediasporting behavior (IMSB), which is a new self-annotated sports dataset. For evaluation, we used the “leave-one-out” cross validation scheme and the results outperformed existing well-known statistical state-of-the-art methods by achieving an improved recognition accuracy of 91.25%, 93.66% and 90.91% when compared with USC-HAD, IMSB, and Mhealth datasets, respectively. The proposed system should be applicable to man–machine interface domains, such as health exercises, robot learning, interactive games and pattern-based surveillance.

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A New Feature Extraction Technique Based on 1D Local Binary Pattern for Gear Fault Detection

Cited by 10 publications

References 11 publications

Inferior alveolar nerve canal segmentation by local features based neural network model

Inferior alveolar nerve canal segmentation by local features based neural network model

HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks

Wearable Inertial Sensors for Daily Activity Analysis Based on Adam Optimization and the Maximum Entropy Markov Model

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