The star identification algorithm usually identifies stars by angular distance matching.However,under high dynamic conditions,the rolling shutter effect distorts the angular distances between the measured and true sta...The star identification algorithm usually identifies stars by angular distance matching.However,under high dynamic conditions,the rolling shutter effect distorts the angular distances between the measured and true star positions,leading to plethoric false matches and requiring complex and time-consuming verification for star identification.Low identification rate hinders the application of low-noise and cost-effective rolling shutter image sensors.In this work,we first study a rolling shutter distortion model of angular distances between stars,and then propose a novel three-stage star identification algorithm to identify distorted star images captured by the rolling shutter star sensor.The first stage uses a modified grid algorithm with adaptive error tolerance and an expanded pattern database to efficiently eliminate spurious matches.The second stage performs angular velocity estimation based on Hough transform to verify the matches that follow the same distortion pattern.The third stage applies a rolling shutter error correction method for further verification.Both the simulation and night sky image test demonstrate the effectiveness and efficiency of our algorithm under high dynamic conditions.The accuracy of angular velocity estimation method by Hough transform is evaluated and the root mean square error is below 0.5(°)/s.Our algorithm achieves a 95.7% identification rate at an angular velocity of 10(°)/s,which is much higher than traditional algorithms.展开更多
Most modern consumer-grade cameras are often equipped with a rolling shutter mechanism,which is becoming increasingly important in computer vision,robotics and autonomous driving applications.However,its temporal-dyna...Most modern consumer-grade cameras are often equipped with a rolling shutter mechanism,which is becoming increasingly important in computer vision,robotics and autonomous driving applications.However,its temporal-dynamic imaging nature leads to the rolling shutter effect that manifests as geometric distortion.Over the years,researchers have made significant progress in developing tractable rolling shutter models,optimization methods,and learning approaches,aiming to remove geometry distortion and improve visual quality.In this survey,we review the recent advances in rolling shutter cameras from two aspects of motion modeling and deep learning.To the best of our knowledge,this is the first comprehensive survey of rolling shutter cameras.In the part of rolling shutter motion modeling and optimization,the principles of various rolling shutter motion models are elaborated and their typical applications are summarized.Then,the applications of deep learning in rolling shutter based image processing are presented.Finally,we conclude this survey with discussions on future research directions.展开更多
基金supported by the National Key Research and Development Program of China(No.2019YFA0706002).
文摘The star identification algorithm usually identifies stars by angular distance matching.However,under high dynamic conditions,the rolling shutter effect distorts the angular distances between the measured and true star positions,leading to plethoric false matches and requiring complex and time-consuming verification for star identification.Low identification rate hinders the application of low-noise and cost-effective rolling shutter image sensors.In this work,we first study a rolling shutter distortion model of angular distances between stars,and then propose a novel three-stage star identification algorithm to identify distorted star images captured by the rolling shutter star sensor.The first stage uses a modified grid algorithm with adaptive error tolerance and an expanded pattern database to efficiently eliminate spurious matches.The second stage performs angular velocity estimation based on Hough transform to verify the matches that follow the same distortion pattern.The third stage applies a rolling shutter error correction method for further verification.Both the simulation and night sky image test demonstrate the effectiveness and efficiency of our algorithm under high dynamic conditions.The accuracy of angular velocity estimation method by Hough transform is evaluated and the root mean square error is below 0.5(°)/s.Our algorithm achieves a 95.7% identification rate at an angular velocity of 10(°)/s,which is much higher than traditional algorithms.
基金This work was supported in part by National Natural Science Foundation of China(Nos.62271410,61901387 and 62001394)the Fundamental Research Funds for the Central Universities,China,and the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University,China(No.CX2022046).
文摘Most modern consumer-grade cameras are often equipped with a rolling shutter mechanism,which is becoming increasingly important in computer vision,robotics and autonomous driving applications.However,its temporal-dynamic imaging nature leads to the rolling shutter effect that manifests as geometric distortion.Over the years,researchers have made significant progress in developing tractable rolling shutter models,optimization methods,and learning approaches,aiming to remove geometry distortion and improve visual quality.In this survey,we review the recent advances in rolling shutter cameras from two aspects of motion modeling and deep learning.To the best of our knowledge,this is the first comprehensive survey of rolling shutter cameras.In the part of rolling shutter motion modeling and optimization,the principles of various rolling shutter motion models are elaborated and their typical applications are summarized.Then,the applications of deep learning in rolling shutter based image processing are presented.Finally,we conclude this survey with discussions on future research directions.
