What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Computer network
Sen-ching S. Cheung mostly deals with Artificial intelligence, Computer vision, Background subtraction, Data compression and Video tracking.
His Artificial intelligence research incorporates themes from Identification and Pattern recognition.
His Computer vision research incorporates elements of Frame, Adaptive filter and Visualization.
His Background subtraction research is multidisciplinary, incorporating elements of Segmentation, Image segmentation, Inpainting, Color space and Median filter.
His study looks at the relationship between Data compression and fields such as Block-matching algorithm, as well as how they intersect with chemical problems.
His study in Video tracking is interdisciplinary in nature, drawing from both Computer security, Authentication, Watermark and Interpolation.
His most cited work include:
- Robust techniques for background subtraction in urban traffic video (611 citations)
- Robust background subtraction with foreground validation for urban traffic video (234 citations)
- Efficient video similarity measurement with video signature (207 citations)
What are the main themes of his work throughout his whole career to date?
Sen-ching S. Cheung mainly focuses on Artificial intelligence, Computer vision, Computer security, Pattern recognition and Computer network.
His research on Computer vision often connects related areas such as Computer graphics.
His study in the field of Privacy software, Biometrics, Cryptography and Information privacy is also linked to topics like Collusion.
His Pattern recognition research includes themes of Cluster analysis and Image retrieval.
The concepts of his Computer network study are interwoven with issues in Distributed computing and The Internet.
Sen-ching S. Cheung combines subjects such as Color space and Median filter with his study of Background subtraction.
He most often published in these fields:
- Artificial intelligence (50.00%)
- Computer vision (34.91%)
- Computer security (16.04%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (50.00%)
- Pattern recognition (15.09%)
- Convolutional neural network (4.72%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Pattern recognition, Convolutional neural network, Artificial neural network and Segmentation.
His research investigates the connection between Artificial intelligence and topics such as Computer vision that intersect with problems in Range.
The Pattern recognition study combines topics in areas such as Head and Deep learning.
His Convolutional neural network research includes elements of Feature, Leverage, Support vector machine, Crowds and Generative model.
His Artificial neural network study incorporates themes from Quantization, Transform coding, Contextual image classification, Data compression and Pose.
His work on Image segmentation as part of his general Segmentation study is frequently connected to Grey matter, thereby bridging the divide between different branches of science.
Between 2018 and 2021, his most popular works were:
- People Counting in Dense Crowd Images Using Sparse Head Detections (27 citations)
- Attention Mechanism Exploits Temporal Contexts: Real-Time 3D Human Pose Reconstruction (11 citations)
- Motion and appearance based background subtraction for freely moving cameras (8 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Computer network
His primary areas of investigation include Artificial intelligence, Pattern recognition, Deep learning, Convolutional neural network and Benchmark.
In his works, he undertakes multidisciplinary study on Artificial intelligence and Autism spectrum disorder.
His studies in Benchmark integrate themes in fields like Artificial neural network, Pose and Word error rate.
Sen-ching S. Cheung interconnects Head, Crowds, Feature and Support vector machine in the investigation of issues within Leverage.
His Noise reduction study introduces a deeper knowledge of Computer vision.
Sen-ching S. Cheung integrates several fields in his works, including Computer vision and Process.
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