Review of local binary pattern operators in image feature extraction

Khaleefah, Shihab Hamad; Mostafa, Salama A.; Nasrudin, Mohammad Faidzul

doi:10.11591/ijeecs.v19.i1.pp23-31

Cited by 20 publications

(12 citation statements)

References 21 publications

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“…FE involves both bottom-layer features (image color and texture) and advanced features (including image generalization and abstraction). Information, such as size and quantity, helps in visualizing asset management (Khaleefah et al, 2020;Saad and Hirakawa, 2020). These features are listed as follows.…”

Section: Feature Extractionmentioning

confidence: 99%

Application of Deep Convolution Network Algorithm in Sports Video Hot Spot Detection

et al. 2022

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Sports videos are blowing up over the internet with enriching material life and the higher pursuit of spiritual life of people. Thus, automatically identifying and detecting helpful information from videos have arisen as a relatively novel research direction. Accordingly, the present work proposes a Human Pose Estimation (HPE) model to automatically classify sports videos and detect hot spots in videos to solve the deficiency of traditional algorithms. Firstly, Deep Learning (DL) is introduced. Then, amounts of human motion features are extracted by the Region Proposal Network (RPN). Next, an HPE model is implemented based on Deep Convolutional Neural Network (DCNN). Finally, the HPE model is applied to motion recognition and video classification in sports videos. The research findings corroborate that an effective and accurate HPE model can be implemented using the DCNN to recognize and classify videos effectively. Meanwhile, Big Data Technology (BDT) is applied to count the playing amounts of various sports videos. It is convinced that the HPE model based on DCNN can effectively and accurately classify the sports videos and then provide a basis for the following statistics of various sports videos by BDT. Finally, a new outlook is proposed to apply new technology in the entertainment industry.

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Section: Feature Extractionmentioning

confidence: 99%

Application of Deep Convolution Network Algorithm in Sports Video Hot Spot Detection

et al. 2022

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“…However, for the threshold denoising method, although the obtained estimated wavelet coefficient has good continuity, when the wavelet coefficient is greater than the threshold, there will be a constant deviation between the wavelet coefficient and the estimated wavelet coefficient. It will affect the proximity between the reconstructed image signal and the actual signal, and the error of the reconstructed signal will increase [8].…”

Section: Image Preprocessingmentioning

confidence: 99%

Research on Image Texture Feature Extraction Based on Digital Twin

2022

Mathematical Problems in Engineering

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The purpose of image smoothing is to improve the visual effect of the image and improve the clarity of the image, so as to make the image more conducive to computer processing and various feature analysis. Because the current technology fails to smooth the preprocessed image, it leads to the extraction of image texture features. The anti-interference performance is weak. For this reason, an image texture feature extraction technology based on the digital twin is proposed. Similarity analysis is carried out through the internal structure of the image, and the image is smoothed by the semisupervised learning method. On the basis of optimizing the denoised image through digital twinning, detect target feature points in the original image, then remove the abnormal and split feature points, assign the direction of image texture feature points, and build a fuzzy back propagation neural network model. Image texture feature extraction technology is implemented. The experimental results show that, compared with the traditional method, the proposed technique has a strong identification of original image features, and has a strong consistency with original data, and has a strong ability to resist the influence of abnormal data, noise, or redundant feature points.

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“…As for now, there are limited literatures on the application of image processing technique to determine the pre-breakdown and breakdown condition. Thus, this work is conducted to analyze the electric field bridging images using digital image processing technique that has been applied in many studies involving mathematical morphology, neural networks, color image processing, feature extraction, image compression, image recognition and knowledge-based image analysis system [15,16]. Digital image processing technique is chosen to visualize the image character and extract the important information [17] from the electric field bridging formation image.…”

Section: Figure 1 Example Of Electric Field Bridging Formation In Rgmentioning

confidence: 99%

Electric field bridging pattern of pre-breakdown and breakdown condition in transformer oil

Mustafa

Ali

Zainuddin

et al. 2020

IJEECS

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Transformer is considered as one of the most important equipment in electrical power system networks. However, most problems occurred in transformer were related to the defects and weakness of the insulation systems. The oils used in transformer act as coolant and insulation purposes hence maintaining the dielectric strength of the transformer. In this work, electric field bridging pattern is observed from pre-breakdown and breakdown condition. The electric field bridging formation was recorded in the experimental setup and images were captured per frame. 193 images were randomly chosen from the whole video frames where 102 images were the pre-breakdown images and 91 images were the breakdown images. This system comprises of four stages: (i) a preprocessing stage to mark the electrodes tips and background subtraction; (ii) a segmentation stage to extract the electric field bridging formation in region of interest; (iii) a feature extraction stage to extract electric field bridging using feature descriptors, area, minor-axis and major-axis length (iv) a classification stage to identify the pre-breakdown and breakdown condition. System performance was evaluated using support vector machine (SVM), k-nearest neighbour (k-NN) and random forest (RF) and SVM provided the most promising accuracy that was 99%. The results show that the combination of three feature descriptors, area, minor-axis and major-axis length are the best features combination in identifying the transformer oil condition. In future work, further studies will be conducted to investigate the pattern of pre- and post-breakdown due to some similarity found in image pattern. Due to that, more feature descriptors will be identified to find a unique pattern between pre- and post-breakdown condition

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Review of local binary pattern operators in image feature extraction

Cited by 20 publications

References 21 publications

Application of Deep Convolution Network Algorithm in Sports Video Hot Spot Detection

Application of Deep Convolution Network Algorithm in Sports Video Hot Spot Detection

Research on Image Texture Feature Extraction Based on Digital Twin

Electric field bridging pattern of pre-breakdown and breakdown condition in transformer oil

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