What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Optics
His primary areas of study are Artificial intelligence, Computer vision, Pattern recognition, Iterative reconstruction and Feature.
Qionghai Dai has included themes like Machine learning and Graph in his Artificial intelligence study.
Qionghai Dai has researched Computer vision in several fields, including Hyperspectral imaging and Surface reconstruction.
The concepts of his Pattern recognition study are interwoven with issues in Contextual image classification, Linear subspace, Categorization and Image retrieval.
His study in Iterative reconstruction is interdisciplinary in nature, drawing from both Epipolar geometry, 3D reconstruction, Image resolution and Image texture.
His work in Feature addresses subjects such as Visualization, which are connected to disciplines such as Stereoscopy, Representation and Image quality.
His most cited work include:
- 3-D Object Retrieval and Recognition With Hypergraph Analysis (453 citations)
- Covariance discriminative learning: A natural and efficient approach to image set classification (359 citations)
- Efficient Parallel Framework for HEVC Motion Estimation on Many-Core Processors (344 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Computer vision, Optics, Pattern recognition and Algorithm.
His research on Artificial intelligence frequently connects to adjacent areas such as Machine learning.
Light field, Depth map, Object, Image resolution and Multiview Video Coding are the subjects of his Computer vision studies.
Qionghai Dai combines subjects such as Phase retrieval and Fourier transform with his study of Optics.
His study in Image retrieval extends to Pattern recognition with its themes.
He most often published in these fields:
- Artificial intelligence (59.49%)
- Computer vision (42.62%)
- Optics (17.72%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (59.49%)
- Optics (17.72%)
- Computer vision (42.62%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Optics, Computer vision, Artificial neural network and Light field.
Artificial intelligence is frequently linked to Pattern recognition in his study.
His work carried out in the field of Computer vision brings together such families of science as Frame and Hyperspectral imaging.
His studies deal with areas such as Epipolar geometry, Image stitching, Algorithm and Lens as well as Light field.
His Microscope course of study focuses on Image sensor and Cardinal point, Relay lens, Image plane and Sample.
His Scattering research is multidisciplinary, incorporating elements of Speckle pattern and Photon.
Between 2017 and 2021, his most popular works were:
- Supervised Hash Coding With Deep Neural Network for Environment Perception of Intelligent Vehicles (194 citations)
- Effective Uyghur Language Text Detection in Complex Background Images for Traffic Prompt Identification (144 citations)
- A Fast Uyghur Text Detector for Complex Background Images (135 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Optics
- Computer vision
His main research concerns Artificial intelligence, Computer vision, Optics, Artificial neural network and Image resolution.
In most of his Artificial intelligence studies, his work intersects topics such as Pattern recognition.
He interconnects Image quality, Image, Quantization and Identification in the investigation of issues within Pattern recognition.
His research integrates issues of Image processing, Multiplexing and Phase modulation in his study of Optics.
His Artificial neural network research integrates issues from Automatic control, Control theory, Control theory and Benchmark.
His Image resolution research incorporates themes from Similarity, Grayscale, Frame rate, Interpolation and Image restoration.
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