Image Intelligent Detection Based on the Gabor Wavelet and the Neural Network

Xu, Yuelei; Liang, Fengmei; Zhang, Gang; Xu, Huipu

doi:10.3390/sym8110130

Cited by 16 publications

(9 citation statements)

References 11 publications

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“…Ince et al [20] used a one-dimensional (1-D) CNN for a real-time motor fault diagnosis. Xu et al [38] proposed a study based on the Gabor wavelet and the neural network to detect the image intelligence. The authors employed the Gabor wavelet transform to extract the features of information from images.…”

Section: Related Workmentioning

confidence: 99%

Fault Diagnosis System for Induction Motors by CNN Using Empirical Wavelet Transform

Hsueh

Ittangihala

et al. 2019

Symmetry

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Detecting the faults related to the operating condition of induction motors is a very important task for avoiding system failure. In this paper, a novel methodology is demonstrated to detect the working condition of a three-phase induction motor and classify it as a faulty or healthy motor. The electrical current signal data is collected for five different types of fault and one normal operating condition of the induction motors. The first part of the methodology illustrates a pattern recognition technique based on the empirical wavelet transform, to transform the raw current signal into two dimensional (2-D) grayscale images comprising the information related to the faults. Second, a deep CNN (Convolutional Neural Network) model is proposed to automatically extract robust features from the grayscale images to diagnose the faults in the induction motors. The experimental results show that the proposed methodology achieves a competitive accuracy in the fault diagnosis of the induction motors and that it outperformed the traditional statistical and other deep learning methods.

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Section: Related Workmentioning

confidence: 99%

Fault Diagnosis System for Induction Motors by CNN Using Empirical Wavelet Transform

Hsueh

Ittangihala

et al. 2019

Symmetry

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“…This is because each Gaussian weights high and also low frequencies in a symmetric way, while the disintegration is coarser at high frequencies. To counter this effect, a set of changed Gabor channels were employed, which are characterized as Gaussians in the log-polar frequency plane [20]. Figure 11a,b shows the modified Gabor filter in the Fourier domain and a 2D plot of cross-sectional coordinates.…”

Section: Secondary Phasementioning

confidence: 99%

“…However, in scenarios 3 and 4, where the intruder has fully obscured their face using some type of transparent, solid, paper, plastic, or leather material and the intruder has been captured in the dark using an analog camera without night vsion facility, all the existing methodologies have failed in detecting the intruder except for the neural networks and two step R-CNN method. Howver, the two step R-CNN method requires the usage of a thermal camera to detect an intruder in the dark night [20]. The proposed system can detect an intruder using an analog camera without night vision capability.…”

Section: Final Phasementioning

confidence: 99%

Smart Home Anti-Theft System: A Novel Approach for Near Real-Time Monitoring and Smart Home Security for Wellness Protocol

Pandya¹,

Ghayvat

Kotecha

et al. 2018

ASI

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The proposed research methodology aims to design a generally implementable framework for providing a house owner/member with the immediate notification of an ongoing theft (unauthorized access to their premises). For this purpose, a rigorous analysis of existing systems was undertaken to identify research gaps. The problems found with existing systems were that they can only identify the intruder after the theft, or cannot distinguish between human and non-human objects. Wireless Sensors Networks (WSNs) combined with the use of Internet of Things (IoT) and Cognitive Internet of Things are expanding smart home concepts and solutions, and their applications. The present research proposes a novel smart home anti-theft system that can detect an intruder, even if they have partially/fully hidden their face using clothing, leather, fiber, or plastic materials. The proposed system can also detect an intruder in the dark using a CCTV camera without night vision capability. The fundamental idea was to design a cost-effective and efficient system for an individual to be able to detect any kind of theft in real-time and provide instant notification of the theft to the house owner. The system also promises to implement home security with large video data handling in real-time. The investigation results validate the success of the proposed system. The system accuracy has been enhanced to 97.01%, 84.13, 78.19%, and 66.5%, in scenarios where a detected intruder had not hidden his/her face, hidden his/her face partially, fully, and was detected in the dark from 85%, 64.13%, 56.70%, and 44.01%.

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“…The image dimension can be reduced to a smaller dimension by retaining significant features [12][13]. The final phase is the classification which is using powerful classifiers such as deep neural networks and the fully connected neural networks [14][15][16].…”

Section: Research Problem and Scopementioning

confidence: 99%

Improving Human Face Recognition using Deep Learning based Image Registration and Multi-Classifier Approaches

Abuzneid

Mahmood

2018

2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA)

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Face detection, registration, and recognition have become a fascinating field for researchers. The motivation behind the enormous interest in the topic is the need to improve the accuracy of many real-time applications. Countless methodologies have been acknowledged and presented in the past years. The complexity of the human face visual and the significant changes based on different effects make it more challenging to design as well as implementing a powerful computational system for object recognition in addition to human face recognition. Using supervised learning often requires extensive training for the computer which results in high execution times. It is an essential step in the face recognition to apply strong preprocessing approaches such as face registration to achieve a high recognition accuracy rate. Although there are exist approaches do both detection and recognition, we believe the absence of a complete end-to-end system capable of performing recognition from an arbitrary scene is in large part due to the difficulty in alignment. Often, the face registration is ignored, with the assumption that the detector will perform a rough alignment, leading to suboptimal recognition v performance. In this research, we presented an enhanced approach to improve human face recognition using a back-propagation neural network (BPNN) and features extraction based on the correlation between the training images. A key contribution of this paper is the generation of a new set called the T-Dataset from the original training data set, which is used to train the BPNN. We generated the T-Dataset using the correlation between the training images without using a common technique of image density. The correlated T-Dataset provides a high distinction layer between the training images, which helps the BPNN to converge faster and achieve better accuracy. Data and features reduction is essential in the face recognition process, and researchers have recently focused on the modern neural network. Therefore, we used using a classical conventional Principal Component Analysis (PCA) and Local Binary Patterns (LBP) to prove that there is a potential improvement even using traditional methods. We applied five distance measurement algorithms and then combined them to obtain the T-Dataset, which we fed into the BPNN. We achieved higher face recognition accuracy with less computational cost compared with the current approach by using reduced image features. We test the proposed framework on two small data sets, the YALE and AT&T data sets, as the ground truth. We achieved tremendous accuracy. Furthermore, we evaluate our method on one of the state-of-the-art benchmark data sets, Labeled Faces in the Wild (LFW), where we produce a competitive face recognition performance. In addition, we presented an enhanced framework to improve the face registration using deep learning model. We used deep architectures such as VGG16 and VGG19 to train our method. We trained our model to learn the transformation parameters (Rotation, vi scaling, and shift...

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Image Intelligent Detection Based on the Gabor Wavelet and the Neural Network

Cited by 16 publications

References 11 publications

Fault Diagnosis System for Induction Motors by CNN Using Empirical Wavelet Transform

Fault Diagnosis System for Induction Motors by CNN Using Empirical Wavelet Transform

Smart Home Anti-Theft System: A Novel Approach for Near Real-Time Monitoring and Smart Home Security for Wellness Protocol

Improving Human Face Recognition using Deep Learning based Image Registration and Multi-Classifier Approaches

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