What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His main research concerns Artificial intelligence, Polygon mesh, Computer vision, Algorithm and Representation.
His work often combines Artificial intelligence and Abstraction studies.
In Polygon mesh, Yu-Kun Lai works on issues like Image segmentation, which are connected to Noise.
His work on RGB color model as part of general Computer vision research is frequently linked to Contextual information, thereby connecting diverse disciplines of science.
His biological study spans a wide range of topics, including Image processing, Segmentation, Autoencoder and Interpolation.
The various areas that Yu-Kun Lai examines in his Representation study include Embedding and Pattern recognition.
His most cited work include:
- Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid (428 citations)
- Fast mesh segmentation using random walks (119 citations)
- CartoonGAN: Generative Adversarial Networks for Photo Cartoonization (112 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Algorithm, Polygon mesh and Pattern recognition.
His study in Artificial intelligence concentrates on Image, Feature, Segmentation, RGB color model and Feature extraction.
His study on Image segmentation is often connected to Parchment as part of broader study in Segmentation.
His Computer vision study combines topics from a wide range of disciplines, such as Computer graphics, Computer graphics and Non-photorealistic rendering.
His Algorithm research is multidisciplinary, incorporating elements of Representation and Mathematical optimization.
Yu-Kun Lai has researched Polygon mesh in several fields, including Autoencoder, Shape analysis and Computer animation.
He most often published in these fields:
- Artificial intelligence (64.44%)
- Computer vision (40.00%)
- Algorithm (24.00%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (64.44%)
- Computer vision (40.00%)
- Image (12.89%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Computer vision, Image, Pattern recognition and Algorithm.
His study involves Deep learning, Feature, RGB color model, Convolutional neural network and Artificial neural network, a branch of Artificial intelligence.
Yu-Kun Lai performs integrative Computer vision and Code research in his work.
The concepts of his Image study are interwoven with issues in Semantics, Feature extraction and Generator.
Yu-Kun Lai has included themes like Depth map, Cognitive neuroscience of visual object recognition, Autoencoder, Benchmark and Geometry processing in his Pattern recognition study.
His Algorithm research integrates issues from Polygon mesh, Pooling, Representation, Extrapolation and Computer animation.
Between 2019 and 2021, his most popular works were:
- Sparse Data Driven Mesh Deformation (30 citations)
- Semantic Labeling and Instance Segmentation of 3D Point Clouds Using Patch Context Analysis and Multiscale Processing (17 citations)
- Sparse MDMO: Learning a Discriminative Feature for Micro-Expression Recognition (16 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Yu-Kun Lai mainly focuses on Artificial intelligence, Pattern recognition, Computer vision, Algorithm and Polygon mesh.
His work on Artificial intelligence is being expanded to include thematically relevant topics such as Structure.
His Pattern recognition research incorporates themes from Cognitive neuroscience of visual object recognition and Autoencoder.
His work on Object, Object detection and Pose as part of general Computer vision study is frequently linked to Code, therefore connecting diverse disciplines of science.
His work carried out in the field of Algorithm brings together such families of science as Basis, Pooling, Piecewise and Interpolation.
His Polygon mesh research is multidisciplinary, incorporating elements of Polygon, Image warping, Rectangle, Boundary and Image stitching.
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.
