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
Outlier mining is an important aspect in data mining and the outlier miningbased on Cook distance is most commonly used. But we know that when the data have multicollinearity,the traditional Cook method is no longer e...Outlier mining is an important aspect in data mining and the outlier miningbased on Cook distance is most commonly used. But we know that when the data have multicollinearity,the traditional Cook method is no longer effective. Considering the excellence of the principalcomponent estimation, we use it to substitute the least squares estimation, and then give the Cookdistance measurement based on principal component estimation, which can be used in outlier mining.At the same time, we have done some research on related theories and application problems.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.
文摘Outlier mining is an important aspect in data mining and the outlier miningbased on Cook distance is most commonly used. But we know that when the data have multicollinearity,the traditional Cook method is no longer effective. Considering the excellence of the principalcomponent estimation, we use it to substitute the least squares estimation, and then give the Cookdistance measurement based on principal component estimation, which can be used in outlier mining.At the same time, we have done some research on related theories and application problems.
