What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Geometry
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.
His most cited work include:
- Dynamic NURBS with geometric constraints for interactive sculpting (218 citations)
- D-NURBS: a physics-based framework for geometric design (172 citations)
- Virtual clay: a real-time sculpting system with haptic toolkits (139 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (39.49%)
- Algorithm (21.59%)
- Computer vision (21.59%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (39.49%)
- Computer vision (21.59%)
- Pattern recognition (13.07%)
In recent papers he was focusing on the following fields of study:
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.
Between 2018 and 2021, his most popular works were:
- Multitask Cascade Convolution Neural Networks for Automatic Thyroid Nodule Detection and Recognition (38 citations)
- Improved Saliency Detection in RGB-D Images Using Two-Phase Depth Estimation and Selective Deep Fusion (30 citations)
- Improved Robust Video Saliency Detection Based on Long-Term Spatial-Temporal Information (28 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Geometry
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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