Panoramic image mosaic technology based on SIFT algorithm in power monitoring

Li, Dun; Han, Dong‐Guk; Zhang, Xiaofeng; Zhang, Li

doi:10.1109/icsai48974.2019.9010294

Cited by 17 publications

(10 citation statements)

References 10 publications

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“…The pixel marked by the black frame is the central pixel P, while the white frame marks the other 16 pixels. If the absolute value of the difference between the gray values of n consecutive pixels and pixel P is greater than the threshold value t, pixel P is a feature point.Feature matching: Feature matching refers to the process of establishing correspondence between feature points in two images 38 . We use the point‐to‐point distance method to find the k pairs of corresponding feature points 39 …”

Section: Proposed Methodsmentioning

confidence: 99%

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Vehicle video surveillance system based on image fusion and parallel computing

Liu

Lyu

Gong

2020

Circuit Theory & Apps

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Summary Autonomous driving has gradually moved towards practical applications in recent years. It is particularly critical to provide reliable real‐time environmental information for autonomous driving systems. At present, vehicle video surveillance systems based on multi‐source video and target detection algorithms can effectively solve these problems. However, the previous vehicle video surveillance systems are often unable to balance the surveillance effect and the surveillance frame rate. Therefore, we will introduce a vehicle video surveillance system based on parallel computing and computer vision in this article. First, multiple fisheye cameras are used to collect surround‐view environmental information. Second, we will use a low‐light camera, infrared thermal imager, and millimeter‐wave radar to provide forward‐view environmental information at night. Correspondingly, we designed the surround‐view image fusion algorithm and the forward‐view image fusion algorithm based on parallel computing. At last, a monocular camera and detection algorithms are used to provide forward‐view detection results. In a word, this vehicle video surveillance system will benefit the practical application of autonomous driving.

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Section: Proposed Methodsmentioning

confidence: 99%

“…Feature matching: Feature matching refers to the process of establishing correspondence between feature points in two images 38 . We use the point‐to‐point distance method to find the k pairs of corresponding feature points 39 …”

Section: Proposed Methodsmentioning

confidence: 99%

Vehicle video surveillance system based on image fusion and parallel computing

Liu

Lyu

Gong

2020

Circuit Theory & Apps

View full text Add to dashboard Cite

show abstract

“…The details are shown in Algorithm. 4 return Error 7: end if 8: return Ok DeleteRecord(): This method first deletes the hash address from the SDB, and then deletes the complete medical record from the IPFS based on recordId. U pdateRecord(): When this method is executed, it first updates the medical data in the IPFS to get a new hash address, and then restores this new hash address to the SDB by calling the AddRecord() method.…”

Section: ) Policy Contract (Psc)mentioning

confidence: 99%

“…However, this form of medical system faces severe risks of privacy disclosure [3]. Therefore, the transformation of the centralized medical data management system to distributed medical data sharing system is an irresistible trend of the whole society [4]. However, since most medical and health institutions are isolated from each other, they store and maintain medical health data, forming data islands.…”

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confidence: 99%

A Blockchain-based Secure Storage Scheme for Medical Information

Sun

Han

et al. 2022

Preprint

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Medical data involves a large amount of personal information and is highly privacy sensitive. In the age of big data, the increasing informatization of healthcare makes it vital that medical information is stored securely and accurately. However, current medical information is subject to the risk of privacy leakage and difficult to share. To address these issues, this paper proposes a healthcare information security storage solution based on Hyperledger Fabric and the Attribute-Based Access Control (ABAC) framework. The scheme first utilizes attributebased access control, which allows dynamic and fine-grained access to medical information, and then stores the medical information in the blockchain, which can be secured and tamperproof by formulating corresponding smart contracts. In addition, this solution also incorporates IPFS technology to relieve the storage pressure of the blockchain. Experiments show that the proposed scheme combining access control of attributes and blockchain technology in this paper can not only ensure the secure storage and integrity of medical information but also has a high throughput when accessing medical information.

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“…People use the Internet to access a large amount of information that is relevant to their lives and work, and language is one of the most direct types of information, so it is particularly important to get the right and important information back to us from the many linguistic messages available. Artificial intelligence (AI), often thought of as computer systems with human-like thinking and capabilities [1] [2], is used in a wide range of applications such as voice chat, autonomous driving, social media, gaming, industry, and even replacing humans in tedious, repetitive tasks [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A Reinforcement Learning-based Offensive semantics Censorship System for Chatbots

Cai¹,

Han²,

Zheng³

et al. 2022

Preprint

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The rapid development of artificial intelligence (AI) technology has enabled large-scale AI applications to land in the market and practice. However, while AI technology has brought many conveniences to people in the productization process, it has also exposed many security issues. Especially, attacks against online learning vulnerabilities of chatbots occur frequently. Therefore, this paper proposes a semantics censorship chatbot system based on reinforcement learning, which is mainly composed of two parts: the Offensive semantics censorship model and the semantics purification model. Offensive semantics review can combine the context of user input sentences to detect the rapid evolution of Offensive semantics and respond to Offensive semantics responses. The semantics purification model For the case of chatting robot models, it has been contaminated by large numbers of offensive semantics, by strengthening the offensive reply learned by the learning algorithm, rather than rolling back to the early versions. In addition, by integrating a once-through learning approach, the speed of semantics purification

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Panoramic image mosaic technology based on SIFT algorithm in power monitoring

Cited by 17 publications

References 10 publications

Vehicle video surveillance system based on image fusion and parallel computing

Vehicle video surveillance system based on image fusion and parallel computing

A Blockchain-based Secure Storage Scheme for Medical Information

A Reinforcement Learning-based Offensive semantics Censorship System for Chatbots

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