What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Mathematical analysis
His main research concerns Artificial intelligence, Computer vision, Optical flow, Motion estimation and Pattern recognition.
His work is connected to Representation, Iterative reconstruction, Robustness, Cognitive neuroscience of visual object recognition and Image processing, as a part of Artificial intelligence.
His Computer vision research is multidisciplinary, incorporating elements of Basis, Algorithm and Facial expression.
His Optical flow research integrates issues from Subspace topology, Gravitational singularity, Mathematical analysis, Control theory and Affine transformation.
His biological study spans a wide range of topics, including Flow and Image segmentation.
He interconnects Tracking system and Computation in the investigation of issues within Pattern recognition.
His most cited work include:
- EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation (1025 citations)
- Computation of component image velocity from local phase information (909 citations)
- Robust online appearance models for visual tracking (895 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, Pattern recognition, Motion estimation and Algorithm.
His Artificial intelligence research focuses on subjects like Computation, which are linked to Constant.
His Pattern recognition research incorporates elements of Cognitive neuroscience of visual object recognition, Representation, Probabilistic logic and Generative model.
His Motion estimation study incorporates themes from Motion, Mathematical analysis and Trajectory.
The various areas that Allan D. Jepson examines in his Algorithm study include Posterior probability, Bayesian probability, Mathematical optimization, Machine learning and Piecewise.
His Optical flow research is multidisciplinary, incorporating perspectives in Flow, Subspace topology, Basis and Affine transformation.
He most often published in these fields:
- Artificial intelligence (80.12%)
- Computer vision (54.22%)
- Pattern recognition (24.70%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (80.12%)
- Pattern recognition (24.70%)
- Symmetry (4.22%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Symmetry, Computer vision and Categorization.
Allan D. Jepson carries out multidisciplinary research, doing studies in Artificial intelligence and Graphics pipeline.
His work deals with themes such as Polygon mesh, Inference, Generative grammar, Generative model and Rendering, which intersect with Pattern recognition.
His Symmetry research also works with subjects such as
- Scene statistics which connect with Image processing,
- Perception that connect with fields like Speech recognition, Basis and Theoretical physics.
His specific area of interest is Computer vision, where Allan D. Jepson studies Image.
He has included themes like Measure and Gestalt psychology in his Categorization study.
Between 2016 and 2021, his most popular works were:
- AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results (20 citations)
- Geometric Disentanglement for Generative Latent Shape Models (19 citations)
- Spatial relationships between contours impact rapid scene classification. (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Mathematical analysis
His main research concerns Artificial intelligence, Superresolution, Pattern recognition, Pixel and Computer vision.
Artificial intelligence and Point are frequently intertwined in his study.
His work in Superresolution tackles topics such as Focus which are related to areas like Magnification.
His Pattern recognition study combines topics in areas such as Histogram, Curvature and Generative model.
His work carried out in the field of Pixel brings together such families of science as Medial axis and Categorization.
His studies in Medial axis integrate themes in fields like Human visual system model, Segmentation, Convolutional neural network and Salience.
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