2014 - Fellow of American Physical Society (APS) Citation For pioneering work in the development of theoretical and numerical methods for highintensity beam dynamics, and geometric approach and algorithms for gyrokinetic theory
2001 - Fellow of Alfred P. Sloan Foundation
His scientific interests lie mostly in Algorithm, Solid modeling, Artificial intelligence, Computational geometry and Spline. Hong Qin has researched Algorithm in several fields, including Geometric primitive, Geometric shape, Mathematical optimization and Finite element method. His studies deal with areas such as Visualization, Visual computing, Polygon mesh, Solid geometry and Polycube as well as Solid modeling.
His Artificial intelligence research incorporates themes from Computer vision and Pattern recognition. His Computational geometry research incorporates elements of Computer Aided Design, Geometric modeling, Computer graphics, Mesh generation and Point. The Spline study combines topics in areas such as Affine transformation and Topology.
Hong Qin spends much of his time researching Artificial intelligence, Algorithm, Computer vision, Computer graphics and Pattern recognition. His study connects Machine learning and Artificial intelligence. His Algorithm study combines topics from a wide range of disciplines, such as Solid modeling, Spline, Finite element method, Subdivision surface and Mathematical optimization.
His Solid modeling research is multidisciplinary, incorporating perspectives in Polycube and Mesh generation. His Computer vision study frequently draws parallels with other fields, such as Surface. In his study, which falls under the umbrella issue of Computer graphics, Geometric design is strongly linked to Geometric modeling.
Hong Qin mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Deep learning and Feature. Many of his studies on Artificial intelligence involve topics that are commonly interrelated, such as Machine learning. Hong Qin has included themes like Character animation, Inverse kinematics and Key frame in his Computer vision study.
His biological study spans a wide range of topics, including Pixel and Complement. In Point cloud, Hong Qin works on issues like Feature vector, which are connected to Algorithm. His biological study deals with issues like Graph, which deal with fields such as Feature selection.
His primary scientific interests are in Artificial intelligence, Pattern recognition, Deep learning, Feature extraction and Computer vision. His study in Feature, Discriminative model, Salient, Convolutional neural network and Robustness is done as part of Artificial intelligence. His work in Pattern recognition addresses subjects such as Artificial neural network, which are connected to disciplines such as Point, Relation, Point cloud, Feature vector and Data mining.
His work in Deep learning tackles topics such as Spatial contextual awareness which are related to areas like Pose, Text mining and Identification. He works mostly in the field of Feature extraction, limiting it down to topics relating to Pooling and, in certain cases, Representation, as a part of the same area of interest. Hong Qin has included themes like Phase retrieval and Key frame in his Computer vision study.
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Dynamic NURBS with geometric constraints for interactive sculpting
Demetri Terzopoulos;Hong Qin.
ACM Transactions on Graphics (1994)
Hongyu Wang;Ying He;Xin Li;Xianfeng Gu.
Computer-aided Design (2008)
Xianfeng Gu;Ying He;Hong Qin.
Graphical Models /graphical Models and Image Processing /computer Vision, Graphics, and Image Processing (2006)
D-NURBS: a physics-based framework for geometric design
Hong Qin;D. Terzopoulos.
IEEE Transactions on Visualization and Computer Graphics (1996)
Virtual clay: a real-time sculpting system with haptic toolkits
Kevin T. McDonnell;Hong Qin;Robert A. Wlodarczyk.
interactive 3d graphics and games (2001)
Shape Topics: A Compact Representation and New Algorithms for 3D Partial Shape Retrieval
Yi Liu;Hongbin Zha;Hong Qin.
computer vision and pattern recognition (2006)
Harmonic volumetric mapping for solid modeling applications
Xin Li;Xiaohu Guo;Hongyu Wang;Ying He.
solid and physical modeling (2007)
Video Saliency Detection via Spatial-Temporal Fusion and Low-Rank Coherency Diffusion
Chenglizhao Chen;Shuai Li;Yongguang Wang;Hong Qin.
IEEE Transactions on Image Processing (2017)
Shape Reconstruction from 3D and 2D Data Using PDE-Based Deformable Surfaces
Ye Duan;Liu Yang;Hong Qin;Dimitris Samaras.
european conference on computer vision (2004)
Dynamic Catmull-Clark subdivision surfaces
Hong Qin;C. Mandal;B.C. Vemuri.
IEEE Transactions on Visualization and Computer Graphics (1998)
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