What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His primary areas of study are Artificial intelligence, Pattern recognition, Computer vision, Convolutional neural network and Image texture.
His Artificial intelligence study often links to related topics such as Bayesian multivariate linear regression.
His work on Feature extraction, Feature vector and Support vector machine as part of general Pattern recognition research is frequently linked to Gaussian process, bridging the gap between disciplines.
His Video tracking, Eye tracking, Object and Tracking study in the realm of Computer vision connects with subjects such as Memory controller.
As a member of one scientific family, Antoni B. Chan mostly works in the field of Convolutional neural network, focusing on Pose and, on occasion, Monocular, Deep learning and Structured prediction.
His Image texture study integrates concerns from other disciplines, such as Motion estimation and Mixture model.
His most cited work include:
- Supervised Learning of Semantic Classes for Image Annotation and Retrieval (838 citations)
- Privacy preserving crowd monitoring: Counting people without people models or tracking (792 citations)
- Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures (334 citations)
What are the main themes of his work throughout his whole career to date?
Antoni B. Chan mostly deals with Artificial intelligence, Computer vision, Pattern recognition, Eye movement and Hidden Markov model.
Artificial intelligence connects with themes related to Machine learning in his study.
The concepts of his Computer vision study are interwoven with issues in Artificial neural network and Set.
His work on Image texture and Mixture model as part of his general Pattern recognition study is frequently connected to Generative model and Expectation–maximization algorithm, thereby bridging the divide between different branches of science.
His research in Eye movement tackles topics such as Eye tracking which are related to areas like Fixation.
His study looks at the intersection of Feature extraction and topics like Feature with Crowd counting.
He most often published in these fields:
- Artificial intelligence (64.29%)
- Computer vision (29.76%)
- Pattern recognition (27.38%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (64.29%)
- Computer vision (29.76%)
- Eye movement (19.05%)
In recent papers he was focusing on the following fields of study:
Antoni B. Chan mainly focuses on Artificial intelligence, Computer vision, Eye movement, Crowd counting and Pattern recognition.
Many of his studies on Artificial intelligence involve topics that are commonly interrelated, such as Machine learning.
His study in Computer vision is interdisciplinary in nature, drawing from both Heuristic and Flow network.
His research integrates issues of Cognitive psychology, Eye tracking and Hidden Markov model in his study of Eye movement.
His work carried out in the field of Crowd counting brings together such families of science as Digit recognition and Discriminative model.
His Image study combines topics from a wide range of disciplines, such as Deep learning and Representation.
Between 2019 and 2021, his most popular works were:
- ROAM: Recurrently Optimizing Tracking Model (21 citations)
- Visual Tracking via Dynamic Memory Networks (10 citations)
- 3D Crowd Counting via Multi-View Fusion with 3D Gaussian Kernels (5 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Antoni B. Chan focuses on Artificial intelligence, Crowd counting, Computer vision, Object and Pattern recognition.
While working on this project, Antoni B. Chan studies both Artificial intelligence and Gaussian function.
In general Computer vision study, his work on Matching, Tracking and Object detection often relates to the realm of Trajectory, thereby connecting several areas of interest.
His Object research incorporates themes from Representation, Benchmark, Component and Interpolation.
The Pattern recognition study combines topics in areas such as Property, Consistency and Projection.
His research in Feature intersects with topics in Feature extraction, Memorization and Eye tracking.
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