What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
His main research concerns Artificial intelligence, Computer vision, Topology optimization, Actuator and Pattern recognition.
His Artificial intelligence study incorporates themes from Dynamic programming and Facade.
His work deals with themes such as Real tree and Geometric shape, which intersect with Computer vision.
His Topology optimization research incorporates themes from Harmonic and Control theory, Sensitivity.
The concepts of his Pattern recognition study are interwoven with issues in Interval tree, Computer-generated imagery and Robustness.
His Stereopsis research includes themes of Level of detail, Interpolation, Graphics processing unit and Benchmark.
His most cited work include:
- On building an accurate stereo matching system on graphics hardware (392 citations)
- Plant Growth Modelling and Applications: The Increasing Importance of Plant Architecture in Growth Models (213 citations)
- Optimized image resizing using seam carving and scaling (151 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Topology optimization are his primary areas of study.
His work in Segmentation, Rendering, Point cloud, Deep learning and Pixel is related to Artificial intelligence.
His biological study spans a wide range of topics, including Point, Curvature, Principal curvature and Normal.
His Computer vision study frequently involves adjacent topics like Visualization.
His Algorithm research incorporates elements of Surface, Mathematical optimization, Kernel and Computer graphics.
His Topology optimization study combines topics in areas such as Optimal design, Control theory and Topology.
He most often published in these fields:
- Artificial intelligence (45.74%)
- Computer vision (30.23%)
- Algorithm (17.83%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (45.74%)
- Pattern recognition (14.73%)
- Computer vision (30.23%)
In recent papers he was focusing on the following fields of study:
Xiaopeng Zhang focuses on Artificial intelligence, Pattern recognition, Computer vision, Deep learning and Algorithm.
The Artificial intelligence study combines topics in areas such as Point and Graph.
His Pattern recognition study integrates concerns from other disciplines, such as Construct, Task and Stroke.
His study deals with a combination of Computer vision and Stage.
His Deep learning study which covers Polygon mesh that intersects with Vertex, Computational science, Function, Computation and Upsampling.
His studies in Algorithm integrate themes in fields like Optical flow, Rewriting, Compact space and Markov chain.
Between 2019 and 2021, his most popular works were:
- Efficient Joint Gradient Based Attack Against SOR Defense for 3D Point Cloud Classification (6 citations)
- Surface Remeshing: A Systematic Literature Review of Methods and Research Directions. (5 citations)
- Realistic Procedural Plant Modeling from Multiple View Images (5 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
His primary areas of investigation include Artificial intelligence, Deep learning, Pattern recognition, Solid modeling and Depth map.
Xiaopeng Zhang performs integrative study on Artificial intelligence and Detector in his works.
His research integrates issues of Algorithm and Point in his study of Deep learning.
His study in the fields of Discriminative model under the domain of Pattern recognition overlaps with other disciplines such as Dirichlet's energy.
His Solid modeling research incorporates elements of Data mining and Iterative reconstruction.
In his study, Computer vision is inextricably linked to Procedural modeling, which falls within the broad field of Iterative reconstruction.
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