Hand gesture recognition method based on HOG-LBP features for mobile devices

Lahiani, Houssem; Néji, Mahmoud

doi:10.1016/j.procs.2018.07.259

Cited by 40 publications

(28 citation statements)

References 18 publications

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“…Öztürk et al [25] deployed an image classification using LBP and texture features. Otherwise, fractal-based texture classification [26], dictionary learning with LBP [27], Histogram of Oriented Gradient (HOG) based LBP [28] were also introduced subsequently.…”

Section: Introductionmentioning

confidence: 99%

An efficient local binary pattern based plantar pressure optical sensor image classification using convolutional neural networks

Wang

et al. 2019

Optik

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The objective of this study was to design and produce highly comfortable shoe products guided by a plantar pressure imaging data-set. Previous studies have focused on the geometric measurement on the size of the plantar, while in this research a plantar pressure optical imaging data-set based classification technology has been developed. In this paper, an improved local binary pattern (LBP) algorithm is used to extract texture-based features and recognize patterns from the data-set. A calculating model of plantar pressure imaging feature area is established subsequently. The data-set is classified by a neural network to guide the generation of various shoe-last surfaces. Firstly, the local binary mode is improved to adapt to the pressure imaging data-set, and the texture-based feature calculation is fully used to accurately generate the feature point set; hereafter, the plantar pressure imaging feature point set is then used to guide the design of last free surface forming. In the presented experiments of plantar imaging, multi-dimensional texture-based features and improved LBP features have been found by a convolution neural network (CNN), and compared with a 21-input-3-output two-layer perceptual neural network. Three feet types are investigated in the experiment, being flatfoot (F) referring to the lack of a normal arch, or arch collapse, Talipes Equinovarus (TE), being the front part of the foot is adduction, calcaneus varus, plantar flexion, or Achilles tendon contracture and Normal (N). This research has achieved an 82% accuracy rate with 10 hidden-layers CNN of rotation invariance LBP (RI-LBP) algorithm using 21 texture-based features by comparing other deep learning methods presented in the literature.

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Section: Introductionmentioning

confidence: 99%

An efficient local binary pattern based plantar pressure optical sensor image classification using convolutional neural networks

Wang

et al. 2019

Optik

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“…CSS determines the color patterns in persons which captures pairwise statistics of spatially contained color distributions. [52], pedestrian detection [77], text recognition [78] • invariant to photometric and geometric transformation [52], [79] • improves the detection accuracy and speed [52] • invariant to illumination condition or shadowing [80] • cannot detect high articulated object perfectly [74] • spatial neighboring pixels context are missed [81] HoG with LBP object detection [75], face recognition [82], hand gesture recognition [83], pedestrian detection [84] • can handle partial occlusion [75], [76] • improves the detection accuracy [75], [76] • unable to handle the articulated deformation of the object [75] HoG with CSS object detection [85], [86], pedestrian detection [87] • improves the classification accuracy for both static images and videos [85] • unable to handle occlusion [85].…”

Section: B: Challengesmentioning

confidence: 99%

Vision-Based Human Detection Techniques: A Descriptive Review

Sumit

Mirjalili

2021

IEEE Access

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“…For taking out the features in a video frame, Histogram Oriented Gradient (HOG) [15] is also considered in our work. The HOG is a feature descriptor that attracts a lot of attention in object recognition applications [30], [49]. The band gesture is distinguished and classified by HOG descriptors through a distribution of pixel gradients in the cell [30].…”

Section: ) Hog Featuresmentioning

confidence: 99%

“…The HOG is a feature descriptor that attracts a lot of attention in object recognition applications [30], [49]. The band gesture is distinguished and classified by HOG descriptors through a distribution of pixel gradients in the cell [30]. By using magnitude and direction of the gradient of a pixel, every single cell of the image has a histogram constructed [30].…”

Section: ) Hog Featuresmentioning

confidence: 99%

“…The band gesture is distinguished and classified by HOG descriptors through a distribution of pixel gradients in the cell [30]. By using magnitude and direction of the gradient of a pixel, every single cell of the image has a histogram constructed [30]. The intensity i(x, y) at every single cell of the input image is used for calculating Gradient m(x, y) and its orientation θ(x, y) [22], [24], [29], [53], [60]:…”

Section: ) Hog Featuresmentioning

confidence: 99%

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A Machine Learning-Based Approach for Counting Blister Cards Within Drug Packages

Bahaghighat¹,

Akbari²,

Xin

2019

IEEE Access

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Nowadays with the rapid development of technologies, machine vision has been used widely in various industries. The main applications of machine vision in industrial product lines are quality control (QC) and quality assurance (QA). The intelligent defects and anomalies recognition throughout the supply chain have come to be an integral part of quality control systems, in particular, in the food and pharmaceutical industries. In these industries, it is a legal requirement in manufacturing processes which can lead to minimizing the total number of defected products as well as maximizing the performance. In this paper, the machine vision has been utilized to monitor and control the proper packaging of drugs in pharmaceutical product lines. The main goal is counting the number of blister cards within a drug package. To tackle this problem, a new model based on object detection, feature extraction, and classification is proposed. Thanks to several strong approaches, such as the Haar cascade, HOG, ORG, Gabor wavelet, Radon transform, KNN, and SVM, and the accuracy over 88% is achieved in our experiments.

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Hand gesture recognition method based on HOG-LBP features for mobile devices

Cited by 40 publications

References 18 publications

An efficient local binary pattern based plantar pressure optical sensor image classification using convolutional neural networks

An efficient local binary pattern based plantar pressure optical sensor image classification using convolutional neural networks

Vision-Based Human Detection Techniques: A Descriptive Review

A Machine Learning-Based Approach for Counting Blister Cards Within Drug Packages

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