The blur in target images caused by camera vibration due to robot motion or hand shaking and by object(s) moving in the background scene is different to deal with in the computer vision system. In this paper, the auth...The blur in target images caused by camera vibration due to robot motion or hand shaking and by object(s) moving in the background scene is different to deal with in the computer vision system. In this paper, the authors study the relation model between motion and blur in the case of object motion existing in video image sequence, and work on a practical computation algorithm for both motion analysis and blur image restoration. Combining the general optical flow and stochastic process, the paper presents an approach by which the motion velocity can be calculated from blurred images. On the other hand, the blurred image can also be restored using the obtained motion information. For solving a problem with small motion limitation on the general optical flow computation, a multiresolution optical flow algorithm based on MAP estimation is proposed. For restoring the blurred image, an iteration algorithm and the obtained motion velocity are used. The experiment shows that the proposed approach for both motion velocity computation and blurred image restoration works well.展开更多
Computer vision techniques, in conjunction with acquisition through remote cameras and unmanned aerial vehicles (UAVs), offer promising non-contact solutions to civil infrastructure condition assessment. The ultimate ...Computer vision techniques, in conjunction with acquisition through remote cameras and unmanned aerial vehicles (UAVs), offer promising non-contact solutions to civil infrastructure condition assessment. The ultimate goal of such a system is to automatically and robustly convert the image or video data into actionable information. This paper provides an overview of recent advances in computer vision techniques as they apply to the problem of civil infrastructure condition assessment. In particular, relevant research in the fields of computer vision, machine learning, and structural engineering is presented. The work reviewed is classified into two types: inspection applications and monitoring applications. The inspection applications reviewed include identifying context such as structural components, characterizing local and global visible damage, and detecting changes from a reference image. The monitoring applications discussed include static measurement of strain and displacement, as well as dynamic measurement of displacement for modal analysis. Subsequently, some of the key challenges that persist toward the goal of automated vision-based civil infrastructure and monitoring are presented. The paper concludes with ongoing work aimed at addressing some of these stated challenges.展开更多
A semi-blind image restoration algorithm is proposed based on reduced non-convex approximation of Luminita Vese and Tony Chan＇s （C-V） denoising model. Compared with C-V denoising model, we modify the fidelity term ...A semi-blind image restoration algorithm is proposed based on reduced non-convex approximation of Luminita Vese and Tony Chan＇s （C-V） denoising model. Compared with C-V denoising model, we modify the fidelity term and add a term on point spread function （PSF）. The function depends on two variables： the image function to be restored u and the standard deviation of Gaussian kernel to be estimated a. Then the problems consist in solving a system with two coupled equations. Compared with the Leah Bar＇s semi-blind image restoration model which must solve three coupled equations, our method only needs to solve two equations. Furthermore, the estimation of f by our algorithm is superior to Leah Bar＇s algorithm. The experimental results demonstrate that the proposed method is effective.展开更多
文摘The blur in target images caused by camera vibration due to robot motion or hand shaking and by object(s) moving in the background scene is different to deal with in the computer vision system. In this paper, the authors study the relation model between motion and blur in the case of object motion existing in video image sequence, and work on a practical computation algorithm for both motion analysis and blur image restoration. Combining the general optical flow and stochastic process, the paper presents an approach by which the motion velocity can be calculated from blurred images. On the other hand, the blurred image can also be restored using the obtained motion information. For solving a problem with small motion limitation on the general optical flow computation, a multiresolution optical flow algorithm based on MAP estimation is proposed. For restoring the blurred image, an iteration algorithm and the obtained motion velocity are used. The experiment shows that the proposed approach for both motion velocity computation and blurred image restoration works well.
基金supported in part by funding from the US Army Corps of Engineers under a project entitled ‘‘Cybermodeling: A Digital Surrogate Approach for Optimal Risk-Based Operations and Infrastructure” (W912HZ-17-2-0024)
文摘Computer vision techniques, in conjunction with acquisition through remote cameras and unmanned aerial vehicles (UAVs), offer promising non-contact solutions to civil infrastructure condition assessment. The ultimate goal of such a system is to automatically and robustly convert the image or video data into actionable information. This paper provides an overview of recent advances in computer vision techniques as they apply to the problem of civil infrastructure condition assessment. In particular, relevant research in the fields of computer vision, machine learning, and structural engineering is presented. The work reviewed is classified into two types: inspection applications and monitoring applications. The inspection applications reviewed include identifying context such as structural components, characterizing local and global visible damage, and detecting changes from a reference image. The monitoring applications discussed include static measurement of strain and displacement, as well as dynamic measurement of displacement for modal analysis. Subsequently, some of the key challenges that persist toward the goal of automated vision-based civil infrastructure and monitoring are presented. The paper concludes with ongoing work aimed at addressing some of these stated challenges.
基金the Knowledge Innovation Program of Chinese Academy of Sciences(No.07A1210101)
文摘A semi-blind image restoration algorithm is proposed based on reduced non-convex approximation of Luminita Vese and Tony Chan＇s （C-V） denoising model. Compared with C-V denoising model, we modify the fidelity term and add a term on point spread function （PSF）. The function depends on two variables： the image function to be restored u and the standard deviation of Gaussian kernel to be estimated a. Then the problems consist in solving a system with two coupled equations. Compared with the Leah Bar＇s semi-blind image restoration model which must solve three coupled equations, our method only needs to solve two equations. Furthermore, the estimation of f by our algorithm is superior to Leah Bar＇s algorithm. The experimental results demonstrate that the proposed method is effective.
基金supported by the National Natural Science Foundation of China(Nos.62225304,61933001,62173031,U20A20225)the Interdisciplinary Research Project for Young Teachers of Fundamental Research Funds for the Central Universities (No.FRF-IDRY-22-029)the Beijing Top Discipline for Artificial Intelligent Science and Engineering,University of Science and Technology Beijing。
