Computer vision based eyewear selector

Safety-Critical Cyber-Physical System (SCCPS) refers to the system that if the system fails or its key functions fail, it will cause casualties, property damage, environmental damage, and other catastrophic consequences. Therefore, it is vital to verify the safety of safety critical systems. In the community, the SCCPS safety verification mainly relies on the statistical model checking methodology, but for SCCPS with extremely high safety requirements, the statistical model checking method is difficult/infeasible to sample the extremely small probability event since the probability of the system violating the safety is very low (rare property). In response to this problem, we propose a new method of statistical model checking for high-safety SCCPS. Firstly, with the CTMC-approximated SCCPS path probability space model, it leverages the maximum likelihood estimation method to learn the parameters of CTMC. Then, the embedded DTMC can be derived from CTMC, and a cross-entropy optimization model based on DTMC can be constructed. Finally, we propose an algorithm of iteratively learning the optimal importance sampling distribution on the discrete path space and an algorithm to check the statistical model of verifying the rare attribute. Eventually, experimental results show that the method proposed in this paper can effectively verify the rare attributes of SCCPS. Under the same sample size, comparing with the heuristic importance sampling methods, the estimated value of this method can be better distributed around the mean value, and the related standard deviation and relative error are reduced by more than an order of magnitude.

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“…A � 0, B � 0, i � 1 (5) while i ≤ N do (6) generate a path ω i according to the pdf f(., p (j) ) (7) if ω i ⊨φ then (8)…”

Section: Related Workmentioning

confidence: 99%

“…erefore, it is vital to analyze and verify the safety and reliability of safety-critical systems, and it is of great significance to the design and development of safety-critical systems. Indeed, it has attracted wide attention from researchers and has extensively grown as a prominent research topic in the community [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Towards a Statistical Model Checking Method for Safety-Critical Cyber-Physical System Verification

Xie

Tan

Fang

et al. 2021

Security and Communication Networks

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“…Previous methods are mostly based on 2D images or videos [1,2,3]. Users can upload their personal pictures and choose glasses they like, then the system will add the wanted pair of glasses onto their photos.…”

Section: Related Workmentioning

confidence: 99%

“…This method is simple and effective, but can only handle frontal view. [3] uses videos as user's input and can control image parameters, but the system requires special hardwares, including 2 cameras, and can only run at 9.5 fps.…”

Section: Related Workmentioning

confidence: 99%

Making 3D Eyeglasses Try-on practical

Tang

Zhang

Tang

et al. 2014

2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

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In this paper, we propose a virtual 3D Eyeglasses Try-on (3DET) system, with efficient, realistic and real-time augmented performance. The 3DET system captures user's performance with the help of depth camera and renders the glasses properly on the video stream immediately after user's selection. The virtual eye-glasses follow with the motion (movement or rotation) of user's head simultaneously. The high efficiency of our 3DET system is achieved by simplifying the eyeglasses matching procedure, making use of the active appearance model (AAM) based face tracking algorithm. This is completely different from existing methods, which usually relies on eye detection. In addition, due to the exploiting of a generic 3D face model during tracking and displaying, the 3DET system can handle occlusion problem easily and render realistic glasses in videos effectively. Experimental results demonstrate that the proposed 3DET system is able to produce superior natural and smooth visual results with virtual glasses fitted on the users face at 30 fps with a high level of accuracy on common hardware.

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“…Virtual try-on fills the gap that the goods cannot be tried on in traditional online shopping. As an important part of network virtual try-on, virtual glasses try-on technology has become a key research issue in this field recently [1][2][3][4]. During virtual glasses try-on process, consumers can select their favorite glasses by comparing the actual wearing effects of different glasses in the online shopping.…”

Section: Introductionmentioning

confidence: 99%

Augmented reality virtual glasses try-on technology based on iOS platform

Zhang

2018

J Image Video Proc.

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With the development of e-commerce, network virtual try-on, as a new online shopping mode, fills the gap that the goods cannot be tried on in traditional online shopping. In the work, we discussed augmented reality virtual glasses try-on technology on iOS platform to achieve optimal purchase of online glasses, improving try-on speed of virtual glasses, user senses of reality, and immersion. Face information was collected by the input device-monocular camera. After face detection by SVM classifier, the local face features were extracted by robust SIFT. Combined with SDM, the feature points were iteratively solved to obtain more accurate feature point alignment model. Through the head pose estimation, the virtual model was accurately superimposed on the human face, thus realizing the try-on of virtual glasses. The above research was applied in iOS glasses try-on APP system to design the try-on system of augmented reality virtual glasses on iOS mobile platform. It is proved that the method can achieve accurate identification of face features and quick try-on of virtual glasses.

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Computer vision based eyewear selector

Cited by 9 publications

References 21 publications

Towards a Statistical Model Checking Method for Safety-Critical Cyber-Physical System Verification

Towards a Statistical Model Checking Method for Safety-Critical Cyber-Physical System Verification

Making 3D Eyeglasses Try-on practical

Augmented reality virtual glasses try-on technology based on iOS platform

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