Adaboost modular tensor locality preservative projection: face detection in video using Adaboost modular‐based tensor locality preservative projections

Rajeshwari, J.; Karibasappa, K.; Gopalkrishna, M. T.

doi:10.1049/iet-cvi.2015.0406

Cited by 11 publications

(5 citation statements)

References 29 publications

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“…en the actual space is consistent with the 3D model on the plane to generate two fused 3D models. Such a virtual reality scene can be displayed on the display [9][10][11]. Augmented reality technology not only shows the real-world information but also displays the virtual information at the same time, and the two types of information complement and superimpose each other.…”

Section: Fan Internal Results and Detection Algorithmmentioning

confidence: 99%

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Detection Algorithm of Wind Power Equipment Video Image Sequence Based on Artificial Intelligence

Kamruzzaman

2022

Security and Communication Networks

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How to detect the data in the video image sequence and analyze it efficiently by using the artificial intelligence method is a frontline topic in the field of computer vision. Global wind energy resources are abundant, widely distributed, clean, and pollution-free and meet the requirements of sustainable economic and social development, so relying on the large-scale development and utilization of wind energy has been the common choice of many countries. A major goal of artificial intelligence research is to enable machines to perform complex tasks that normally require human intelligence to perform. The purpose of this study is to detect the video image sequence of wind power equipment based on artificial intelligence and analyze the effectiveness of the algorithm. In this study, augmented reality is used as an auxiliary means. According to the characteristics of the video image inside the fan, a moving target region detection algorithm based on the background difference method is proposed. The algorithm uses the difference between the current image and the background image, uses the first-order Kalman filter to update the dynamic background image, and then uses the adaptive threshold method to segment the moving region. After filtering, the moving target area can be obtained. The results show that the positive detection rate of 980 test samples is 99.6%, and the training time is only 3.785. It is concluded that the accuracy and the number of support vectors of this algorithm are better than other algorithms in the case of the same value of C. It shows that this study has a good detection effect. It provides an effective method for video image sequence detection of wind power equipment.

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Section: Fan Internal Results and Detection Algorithmmentioning

confidence: 99%

“…. ., 2 10 to reduce the range of C value. e standard SVM classifier and PCA-SVM classifier are trained, as shown in Table 3.…”

Section: Training Sample Number Analysis Of Video Imagementioning

confidence: 99%

Detection Algorithm of Wind Power Equipment Video Image Sequence Based on Artificial Intelligence

Kamruzzaman

2022

Security and Communication Networks

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“…where, w i is a diagonal element. In the mixed denoising, the residual can be divided into two categories: for the noise contaminated by Gauss noise, the residual error basically obeys the Gauss distribution and can remain unchanged, that is, the weight is approximately 1; Residues at impact noise points should be weighted to reduce the heavy tail distribution [15]. From the above analysis, f (e i ) can be set to f (e i ) = (w / i e i ) , and the improved mixed noise denoising model is:…”

Section: Methods Mixed Denoising Modelmentioning

confidence: 99%

Face recognition method based on GA-BP neural network algorithm

Liu

Ashraf²

2018

Open Physics

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In order to recognize faces, face recognition methods need to be studied. When a face is identified by the current method, the image denoising effect is poor, the face image recognition result thus has error, the time used to recognize the face image is long, the signal to noise ratio, the recognition result and the recognition efficiency are low. Based on the GA-BP neural network algorithm, a face recognition method is proposed. A mixed denoising model of face images is constructed by combining dictionary based sparse representation with non-local similarity. The principal component analysis method is used to extract the feature of the face image after denoising and staining the eigenvector of the face image. The GA-BP neural network algorithm is used to optimize the initial weights and thresholds so as to achieve the optimal value. The feature vectors of face images are ted into the genetic neural network to complete face recognition. Experimental results show that the proposed method has high signal-to-noise ratio, accuracy and recognition efficiency.

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“…The AdaBoost-based face detection method boasts a high detection rate and a low false detection rate for images of frontal faces in simple backgrounds. However, this method faces several defects: the long training process, the proportional growth between detection rate and false detection rate, and the poor performance on images of side faces with complex backgrounds [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A Face Detection Method Based on Skin Color Model and Improved AdaBoost Algorithm

Yang¹,

Liang²,

Zhou³

et al. 2020

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This paper integrates skin color model and improved AdaBoost into a face detection method for high-resolution images with complex backgrounds. Firstly, the skin color areas were detected in a multi-color space. Each image was subject to adaptive brightness compensation, and converted into the YCbCr space, and a skin color model was established to solve face similarity. After eliminating the background interference by morphological method, the skin color areas were segmented to obtain the candidate face areas. Next, the inertia weight control factors and random search factor were introduced to optimize the global search ability of particle swarm optimization (PSO). The improved PSO was adopted to optimize the initial connection weights and output thresholds of the neural network. After that, a strong AdaBoost classifier was designed based on optimized weak BPNN classifiers, and the weight distribution strategy of AdaBoost was further improved. Finally, the improved AdaBoost was employed to detect the final face areas among the candidate areas. Simulation results show that our face detection method achieved high detection rate at a fast speed, and lowered false detection rate and missed detection rate.

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Adaboost modular tensor locality preservative projection: face detection in video using Adaboost modular‐based tensor locality preservative projections

Cited by 11 publications

References 29 publications

Detection Algorithm of Wind Power Equipment Video Image Sequence Based on Artificial Intelligence

Detection Algorithm of Wind Power Equipment Video Image Sequence Based on Artificial Intelligence

Face recognition method based on GA-BP neural network algorithm

A Face Detection Method Based on Skin Color Model and Improved AdaBoost Algorithm

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