As the development of machine vision technology, the color line-scan system is widely applied in the on-line inspection. Due to the non-uniform gray scale and color distortion of the image acquired by the system, the ...As the development of machine vision technology, the color line-scan system is widely applied in the on-line inspection. Due to the non-uniform gray scale and color distortion of the image acquired by the system, the image correction is needed to reduce the problem of image processing and the stability system. Based on reasons mentioned above, a method that using polynomial fitting to correct the image is presented to solve the problem in this paper. The method has been used in the automatic optical inspection of PCB, and has been proved to be effective. So this method will have a potential application to the development of the color line-scan machine vision system.展开更多
Optical flow estimation is still an important task in computer vision with many interesting applications.However,the results obtained by most of the optical flow techniques are affected by motion discontinuities or il...Optical flow estimation is still an important task in computer vision with many interesting applications.However,the results obtained by most of the optical flow techniques are affected by motion discontinuities or illumination changes.In this paper,we introduce a brightness correction field combined with a gradient constancy constraint to reduce the sensibility to brightness changes between images to be estimated.The advantage of this brightness correction field is its simplicity in terms of computational complexity and implementation.By analyzing the deficiencies of the traditional total variation regularization term in weakly textured areas,we also adopt a structure-adaptive regularization based on the robust Huber norm to preserve motion discontinuities.Finally,the proposed energy functional isminimized by solving its corresponding Euler-Lagrange equation in a more effective multi-resolution scheme,which integrates the twice downsampling strategy with a support-weight median filter.Numerous experiments show that our method is more effective and produces more accurate results for optical flow estimation.展开更多
文摘As the development of machine vision technology, the color line-scan system is widely applied in the on-line inspection. Due to the non-uniform gray scale and color distortion of the image acquired by the system, the image correction is needed to reduce the problem of image processing and the stability system. Based on reasons mentioned above, a method that using polynomial fitting to correct the image is presented to solve the problem in this paper. The method has been used in the automatic optical inspection of PCB, and has been proved to be effective. So this method will have a potential application to the development of the color line-scan machine vision system.
基金Project supported by the National Natural Science Foundation of China (No.U0935004)an IDeA Network of Biomedical Research Excellence (INBRE) grant from the National Institutes of Health (NIH) (No.5P20RR01647206)
文摘Optical flow estimation is still an important task in computer vision with many interesting applications.However,the results obtained by most of the optical flow techniques are affected by motion discontinuities or illumination changes.In this paper,we introduce a brightness correction field combined with a gradient constancy constraint to reduce the sensibility to brightness changes between images to be estimated.The advantage of this brightness correction field is its simplicity in terms of computational complexity and implementation.By analyzing the deficiencies of the traditional total variation regularization term in weakly textured areas,we also adopt a structure-adaptive regularization based on the robust Huber norm to preserve motion discontinuities.Finally,the proposed energy functional isminimized by solving its corresponding Euler-Lagrange equation in a more effective multi-resolution scheme,which integrates the twice downsampling strategy with a support-weight median filter.Numerous experiments show that our method is more effective and produces more accurate results for optical flow estimation.
