His main research concerns Artificial intelligence, Computer vision, Mathematical optimization, Pattern recognition and Biometrics. The Artificial intelligence study combines topics in areas such as Algorithm and Pure mathematics. His research in Computer vision intersects with topics in Matrix decomposition, Simple and Pattern recognition.
His Mathematical optimization research integrates issues from Outlier, Robustness and Maxima and minima. His work on Feature vector as part of general Pattern recognition research is frequently linked to Key, thereby connecting diverse disciplines of science. Hongdong Li focuses mostly in the field of Biometrics, narrowing it down to matters related to Feature and, in some cases, Canonical correlation, Biclustering, Similarity and Motion.
His primary areas of study are Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Mathematical optimization. Feature extraction, Benchmark, Segmentation, Motion estimation and Object are the primary areas of interest in his Artificial intelligence study. Hongdong Li usually deals with Computer vision and limits it to topics linked to Robustness and Outlier.
His studies deal with areas such as Embedding, Feature, Invariant and Biometrics as well as Pattern recognition. The various areas that Hongdong Li examines in his Algorithm study include Perspective and Cluster analysis. Hongdong Li has included themes like Factorization, Real image and Convex optimization in his Mathematical optimization study.
His primary scientific interests are in Artificial intelligence, Computer vision, Pattern recognition, Benchmark and Pose. His work in Image, 3D reconstruction, Segmentation, Feature extraction and Convolutional neural network are all subfields of Artificial intelligence research. Hongdong Li combines subjects such as Convolution, Robustness, Outlier and Piecewise with his study of Computer vision.
His work carried out in the field of Pattern recognition brings together such families of science as Embedding, Recurrent neural network, Ranking and Cluster analysis. His Benchmark study deals with Deep learning intersecting with Sign, Natural language processing and Graph. His Pose research is multidisciplinary, incorporating elements of Correspondence problem, Focus and Algorithm.
Hongdong Li spends much of his time researching Artificial intelligence, Computer vision, Benchmark, Pattern recognition and Robustness. He performs integrative study on Artificial intelligence and Key in his works. As part of the same scientific family, Hongdong Li usually focuses on Computer vision, concentrating on Metric and intersecting with Leverage.
Hongdong Li works mostly in the field of Benchmark, limiting it down to topics relating to Image and, in certain cases, Generalization, Complement, Representation and Biometrics, as a part of the same area of interest. His work in Robustness tackles topics such as Pose which are related to areas like Computational geometry, Correspondence problem, Outlier, Synthetic data and Multi camera. His Feature research focuses on Algorithm and how it relates to Vehicle dynamics and Solver.
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The Visual Object Tracking VOT2016 Challenge Results
Matej Kristan;Aleš Leonardis;Jiři Matas;Michael Felsberg.
european conference on computer vision (2016)
Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration
Jiaolong Yang;Hongdong Li;Dylan Campbell;Yunde Jia.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2016)
Go-ICP: Solving 3D Registration Efficiently and Globally Optimally
Jiaolong Yang;Hongdong Li;Yunde Jia.
international conference on computer vision (2013)
Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices
Sadeep Jayasumana;Richard Hartley;Mathieu Salzmann;Hongdong Li.
computer vision and pattern recognition (2013)
Neural Aggregation Network for Video Face Recognition
Jiaolong Yang;Peiran Ren;Dongqing Zhang;Dong Chen.
computer vision and pattern recognition (2017)
Five-Point Motion Estimation Made Easy
Hongdong Li;R. Hartley.
international conference on pattern recognition (2006)
A Simple Prior-Free Method for Non-rigid Structure-from-Motion Factorization
Yuchao Dai;Hongdong Li;Mingyi He.
International Journal of Computer Vision (2014)
Deep Subspace Clustering Networks
Pan Ji;Tong Zhang;Hongdong Li;Mathieu Salzmann.
neural information processing systems (2017)
Multiple views gait recognition using View Transformation Model based on optimized Gait Energy Image
Worapan Kusakunniran;Qiang Wu;Hongdong Li;Jian Zhang.
international conference on computer vision (2009)
The 3D-3D Registration Problem Revisited
Hongdong Li;R. Hartley.
international conference on computer vision (2007)
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