In response to the challenges posed by insufficient real-time performance and suboptimal matching accuracy of traditional feature matching algorithms within automotive panoramic surround view systems,this paper has pr...In response to the challenges posed by insufficient real-time performance and suboptimal matching accuracy of traditional feature matching algorithms within automotive panoramic surround view systems,this paper has proposed a high-performance dimension reduction parallel matching algorithm that integrates Principal Component Analysis(PCA)and Dual-Heap Filtering(DHF).The algorithm employs PCA to map the feature points into the lower-dimensional space and employs the square of Euclidean distance for feature matching,which significantly reduces computational complexity.To ensure the accuracy of feature matching,the algorithm utilizes Dual-Heap Filtering to filter and refine matched point pairs.To further enhance matching speed and make optimal use of computational resources,the algorithm introduces a multi-core parallel matching strategy,greatly elevating the efficiency of feature matching.Compared to Scale-Invariant Feature Transform(SIFT)and Speeded Up Robust Features(SURF),the proposed algorithm reduces matching time by 77%to 80%and concurrently enhances matching accuracy by 5%to 15%.Experimental results demonstrate that the proposed algorithmexhibits outstanding real-time matching performance and accuracy,effectivelymeeting the feature-matching requirements of automotive panoramic surround view systems.展开更多
Rotational computed laminography(CL)has broad application potential in three-dimensional imaging of plate-like objects because it only requires X-rays to pass through the tested object in the thickness direction durin...Rotational computed laminography(CL)has broad application potential in three-dimensional imaging of plate-like objects because it only requires X-rays to pass through the tested object in the thickness direction during the imaging process.In this study,a rectangular cross-section field-of-view rotational CL(RC-CL)is proposed for circuit board imaging.Compared to other rotational CL systems,the field of view is the largest and most suitable for rectangular circuit boards.Meanwhile,as the imaging geometry of RC-CL is significantly different from that of cone-beam CT,the Feldkamp-Davis-Kress(FDK)reconstruction algorithm cannot be used directly.However,transferring the projection data to fit into the CBCT geometry using two-dimensional interpolation introduces interpolation errors.Therefore,an FDK-type analytical reconstruction algorithm applicable to RC-CL was developed.The effectiveness of the method was validated through numerical experiments,and the influence of the tilt angle on the reconstruction results was analyzed.Finally,the RC-CL technique was applied to real defect detection research on circuit boards.展开更多
基金the National Natural Science Foundation of China(61803206)the Key R&D Program of Jiangsu Province(BE2022053-2)the Nanjing Forestry University Youth Science and Technology Innovation Fund(CX2018004)for partly funding this project.
文摘In response to the challenges posed by insufficient real-time performance and suboptimal matching accuracy of traditional feature matching algorithms within automotive panoramic surround view systems,this paper has proposed a high-performance dimension reduction parallel matching algorithm that integrates Principal Component Analysis(PCA)and Dual-Heap Filtering(DHF).The algorithm employs PCA to map the feature points into the lower-dimensional space and employs the square of Euclidean distance for feature matching,which significantly reduces computational complexity.To ensure the accuracy of feature matching,the algorithm utilizes Dual-Heap Filtering to filter and refine matched point pairs.To further enhance matching speed and make optimal use of computational resources,the algorithm introduces a multi-core parallel matching strategy,greatly elevating the efficiency of feature matching.Compared to Scale-Invariant Feature Transform(SIFT)and Speeded Up Robust Features(SURF),the proposed algorithm reduces matching time by 77%to 80%and concurrently enhances matching accuracy by 5%to 15%.Experimental results demonstrate that the proposed algorithmexhibits outstanding real-time matching performance and accuracy,effectivelymeeting the feature-matching requirements of automotive panoramic surround view systems.
基金supported by the National Key Research and Development Program of China(No.2022YFF0607802)。
文摘Rotational computed laminography(CL)has broad application potential in three-dimensional imaging of plate-like objects because it only requires X-rays to pass through the tested object in the thickness direction during the imaging process.In this study,a rectangular cross-section field-of-view rotational CL(RC-CL)is proposed for circuit board imaging.Compared to other rotational CL systems,the field of view is the largest and most suitable for rectangular circuit boards.Meanwhile,as the imaging geometry of RC-CL is significantly different from that of cone-beam CT,the Feldkamp-Davis-Kress(FDK)reconstruction algorithm cannot be used directly.However,transferring the projection data to fit into the CBCT geometry using two-dimensional interpolation introduces interpolation errors.Therefore,an FDK-type analytical reconstruction algorithm applicable to RC-CL was developed.The effectiveness of the method was validated through numerical experiments,and the influence of the tilt angle on the reconstruction results was analyzed.Finally,the RC-CL technique was applied to real defect detection research on circuit boards.
