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
Measuring the amount of vegetation in a given area on a large scale has long been accomplished using satellite and aerial imaging systems.These methods have been very reliable in measuring vegetation coverage accurate...Measuring the amount of vegetation in a given area on a large scale has long been accomplished using satellite and aerial imaging systems.These methods have been very reliable in measuring vegetation coverage accurately at the top of the canopy,but their capabilities are limited when it comes to identifying green vegetation located beneath the canopy cover.Measuring the amount of urban and suburban vegetation along a street network that is partially beneath the canopy has recently been introduced with the use of Google Street View(GSV)images,made accessible by the Google Street View Image API.Analyzing green vegetation through the use of GSV images can provide a comprehensive representation of the amount of green vegetation found within geographical regions of higher population density,and it facilitates an analysis performed at the street-level.In this paper we propose a fine-tuned color based image filtering and segmentation technique and we use it to define and map an urban green environment index.We deployed this image processing method and,using GSV images as a high-resolution GIS data source,we computed and mapped the green index of Milwaukee County,a 3,082 km^(2) urban/suburban county in Wisconsin.This approach generates a high-resolution street-level vegetation estimate that may prove valuable in urban planning and management,as well as for researchers investigating the correlation between environmental factors and human health outcomes.展开更多
APT attacks are prolonged and have multiple stages, and they usually utilize zero-day or one-day exploits to be penetrating and stealthy. Among all kinds of security tech- niques, provenance tracing is regarded as an ...APT attacks are prolonged and have multiple stages, and they usually utilize zero-day or one-day exploits to be penetrating and stealthy. Among all kinds of security tech- niques, provenance tracing is regarded as an important approach to attack investigation, as it discloses the root cause, the attacking path, and the results of attacks. However, existing techniques either suffer from the limitation of only focusing on the log type, or are high- ly susceptible to attacks, which hinder their applications in investigating APT attacks. We present CAPT, a context-aware provenance tracing system that leverages the advantages of virtualization technologies to transparently collect system events and network events out of the target machine, and processes them in the specific host which introduces no space cost to the target. CAPT utilizes the contexts of collected events to bridge the gap between them, and provides a panoramic view to the attack investigation. Our evaluation results show that CAPT achieves the efi＇ective prov- enance tracing to the attack cases, and it only produces 0.21 MB overhead in 8 hours. With our newly-developed technology, we keep the run-time overhead averages less than 4%.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This work was supported by the National Science Foundation [DUE-1129056]This research was completed under the University of Wisconsin-Milwaukee’s Undergraduate Research in Biology and Mathematics(UBM)Program and was supported by a grant from the National Science Foundation DUE-1129056.Additional support was provided from the University of Wisconsin-Milwaukee’s Support For Undergraduate Research Fellowship(SURF),issued by UW-Milwaukee’s Office of Undergraduate Research.The authors of this paper would like to thank Prof.Gabriella Pinter,Prof.Erica Young and Prof.John Berges for their invaluable support.Finally,the authors would like recognize Google LLC for its publicly available image resource and street view API,without which this investigation would not have been possible.
文摘Measuring the amount of vegetation in a given area on a large scale has long been accomplished using satellite and aerial imaging systems.These methods have been very reliable in measuring vegetation coverage accurately at the top of the canopy,but their capabilities are limited when it comes to identifying green vegetation located beneath the canopy cover.Measuring the amount of urban and suburban vegetation along a street network that is partially beneath the canopy has recently been introduced with the use of Google Street View(GSV)images,made accessible by the Google Street View Image API.Analyzing green vegetation through the use of GSV images can provide a comprehensive representation of the amount of green vegetation found within geographical regions of higher population density,and it facilitates an analysis performed at the street-level.In this paper we propose a fine-tuned color based image filtering and segmentation technique and we use it to define and map an urban green environment index.We deployed this image processing method and,using GSV images as a high-resolution GIS data source,we computed and mapped the green index of Milwaukee County,a 3,082 km^(2) urban/suburban county in Wisconsin.This approach generates a high-resolution street-level vegetation estimate that may prove valuable in urban planning and management,as well as for researchers investigating the correlation between environmental factors and human health outcomes.
基金partially supported by the NSFC-General Technology Basic Research Joint Fund (U1536204)the National Key Technologies R&D Program (2014BAH41B00)+3 种基金the National Nature Science Foundation of China (61672394 61373168 61373169)the National High-tech R&D Program of China (863 Program) (2015AA016004)
文摘APT attacks are prolonged and have multiple stages, and they usually utilize zero-day or one-day exploits to be penetrating and stealthy. Among all kinds of security tech- niques, provenance tracing is regarded as an important approach to attack investigation, as it discloses the root cause, the attacking path, and the results of attacks. However, existing techniques either suffer from the limitation of only focusing on the log type, or are high- ly susceptible to attacks, which hinder their applications in investigating APT attacks. We present CAPT, a context-aware provenance tracing system that leverages the advantages of virtualization technologies to transparently collect system events and network events out of the target machine, and processes them in the specific host which introduces no space cost to the target. CAPT utilizes the contexts of collected events to bridge the gap between them, and provides a panoramic view to the attack investigation. Our evaluation results show that CAPT achieves the efi＇ective prov- enance tracing to the attack cases, and it only produces 0.21 MB overhead in 8 hours. With our newly-developed technology, we keep the run-time overhead averages less than 4%.
