A versatile major axis voted method for efficient ellipse detection

Sawala, Suyog; Ragothaman, Srikanth; Narasimhan, Shankar; Basavaraj, Madivala G.

doi:10.1016/j.patrec.2018.01.016

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…is parallel information processing mechanism makes the model have excellent fault tolerance performance and can effectively improve the recognition probability of the application system [14]. And the model also has the performance of hierarchical storage, which can make it have the characteristics of recovering extracted features [15], making it have excellent adaptability and robustness. erefore, this model is often used in recognition systems [16].…”

Section: Application Of Neural Networkmentioning

confidence: 99%

Cloud Data Access Prevention Method in Face Recognition Technology Based on Computer Vision

Qian

Wang

2022

Security and Communication Networks

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With the development of cloud technology and the innovation of information network technology, people’s dependence on the network has gradually increased, and there are some loopholes in cloud data access. The traditional account permission model can no longer meet the conditions of cloud data access alone. If the visitor temporarily leaves the computer or goes out in an emergency, the data are likely to be leaked. Based on the importance and concern of this issue, some scholars have proposed an authentication system combined with biometric face recognition, but the traditional face recognition system has certain security risks. Such as using face pictures and videos to deceive the system, tampering with face templates, etc. Based on this, this paper proposes an encrypted face authentication system based on CNN neural network. Through the authentication of face data, the content transmission of each part is carried out in the form of ciphertext to ensure the security of information. The experimental results in this paper show that the authentication accuracy rate of DeepID is 94% when it is not encrypted, and the authentication accuracy rate decreases slightly after encryption, which is 93.3%. It is similar in other cases. When the network structure and data set remain unchanged, encryption reduces the authentication accuracy rate by 0.3%–2.4%. It can be seen that the scheme proposed in this chapter improves the system security at the cost of a smaller accuracy rate.

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Section: Application Of Neural Networkmentioning

confidence: 99%

Cloud Data Access Prevention Method in Face Recognition Technology Based on Computer Vision

Qian

Wang

2022

Security and Communication Networks

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“…In recent years, computer vision methods have raised great interest in the manufacture of printed circuit boards (PCBs), which is one of the backbones of the electronics manufacturing industry. As the layout of a PCB and its manufacturing process become more complicated and the products become smaller and more integrated, an automatic optical inspection (AOI) system containing sensors, processors and efficient algorithms becomes highly desirable [1,2].…”

Section: Introductionmentioning

confidence: 99%

A fast PCB hole detection method based on geometric features

Zhu

2020

Meas. Sci. Technol.

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Computer vision methods have raised great interest in the manufacture of printed circuit boards (PCBs). The detection and localization of circular holes on PCBs are very important for positioning or defect detection. In this paper, a new method for such detection is presented. The proposed method first uses an adaptive edge detector to obtain the edge image. Then it utilizes the symmetry of the circle and transforms the PCB image into distance space where a Euclidean distance relationship is shown. Then a rapid circle center search (RCCS) algorithm is used to locate the center of each circle. This algorithm first scans the possible locations of each center and then filters out false selections by measuring how close the candidate center is to the contour with the Hausdorff distance. In addition, the proposed method is compared with the method based on the circular Hough transform for multiple test images in terms of speed and accuracy.

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