The scientist’s investigation covers issues in Artificial intelligence, Transport engineering, Global Positioning System, Computer vision and Simulation. His Pattern recognition research extends to Artificial intelligence, which is thematically connected. His Public transport study in the realm of Transport engineering interacts with subjects such as Poison control.
His work deals with themes such as Computation, Calibration and Big data, which intersect with Global Positioning System. His Computer vision study integrates concerns from other disciplines, such as Positioning system, Multidimensional scaling and Distance matrix. His Simulation research focuses on Stadium and how it connects with Cellular automaton and Process.
His primary areas of study are Artificial intelligence, Computer vision, Pattern recognition, Global Positioning System and Transport engineering. His Artificial intelligence research focuses on Feature extraction, Feature, Image, Deep learning and Pixel. His Computer vision research incorporates themes from Robustness and Laser scanning.
His study ties his expertise on Cluster analysis together with the subject of Pattern recognition. His study in Global Positioning System is interdisciplinary in nature, drawing from both Real-time computing and Data mining. His Transport engineering study combines topics in areas such as Big data, Trajectory and Geographic information system.
His primary scientific interests are in Artificial intelligence, Artificial neural network, Remote sensing, Pattern recognition and Computer vision. His study in Deep learning, Feature, Multispectral image, Convolutional neural network and Segmentation are all subfields of Artificial intelligence. While the research belongs to areas of Artificial neural network, Qingquan Li spends his time largely on the problem of Convolution, intersecting his research to questions surrounding Modal, Multispectral pattern recognition and Panchromatic film.
His work in the fields of Remote sensing and Lidar overlaps with other areas such as Cloud cover. His Pattern recognition research integrates issues from Pixel, Cluster analysis and Hyperspectral image classification. His research in the fields of Image retrieval and Image overlaps with other disciplines such as Inertial frame of reference.
Artificial intelligence, Artificial neural network, Deep learning, Convolutional neural network and Computer vision are his primary areas of study. Qingquan Li studies Artificial intelligence, focusing on Feature extraction in particular. His Artificial neural network research is multidisciplinary, incorporating elements of Land cover, Multispectral image, Remote sensing and Structure from motion.
His Deep learning study deals with Feature intersecting with Consistency, Asynchronous communication, Discriminative model and Algorithm. His studies deal with areas such as Real image, Convolution and Wearable computer as well as Convolutional neural network. His Computer vision research is multidisciplinary, relying on both Visualization and User experience design.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
A comparative analysis of image fusion methods
Zhijun Wang;D. Ziou;C. Armenakis;D. Li.
IEEE Transactions on Geoscience and Remote Sensing (2005)
CrackTree: Automatic crack detection from pavement images
Qin Zou;Yu Cao;Qingquan Li;Qingzhou Mao.
Pattern Recognition Letters (2012)
A Sensor-Fusion Drivable-Region and Lane-Detection System for Autonomous Vehicle Navigation in Challenging Road Scenarios
Qingquan Li;Long Chen;Ming Li;Shih-Lung Shaw.
IEEE Transactions on Vehicular Technology (2014)
Optimizing the Locations of Electric Taxi Charging Stations: a Spatial-temporal Demand Coverage Approach
Wei Tu;Qingquan Li;Qingquan Li;Zhixiang Fang;Shih lung Shaw;Shih lung Shaw.
Transportation Research Part C-emerging Technologies (2016)
Accessibility impacts of China’s high-speed rail network
Jing Cao;Jing Cao;Xiaoyue Cathy Liu;Yinhai Wang;Qingquan Li;Qingquan Li.
Journal of Transport Geography (2013)
A Novel Ranking-Based Clustering Approach for Hyperspectral Band Selection
Sen Jia;Guihua Tang;Jiasong Zhu;Qingquan Li.
IEEE Transactions on Geoscience and Remote Sensing (2016)
Finding Reliable Shortest Paths in Road Networks Under Uncertainty
Bi Yu Chen;Bi Yu Chen;William H. K. Lam;William H. K. Lam;Agachai Sumalee;Qingquan Li.
Networks and Spatial Economics (2013)
Map-matching algorithm for large-scale low-frequency floating car data
Bi Yu Chen;Hui Yuan;Qingquan Li;William H. K. Lam.
International Journal of Geographical Information Science (2014)
Automated Extraction of Road Markings from Mobile Lidar Point Clouds
Bisheng Yang;Lina Fang;Qingquan Li;Jonathan Li.
Photogrammetric Engineering and Remote Sensing (2012)
Zooming into individuals to understand the collective: A review of trajectory-based travel behaviour studies
Yang Yue;Tian Lan;Anthony G.O. Yeh;Qing-Quan Li.
Travel behaviour and society (2014)
Profile was last updated on December 6th, 2021.
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