What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary scientific interests are in Artificial intelligence, Computer vision, Machine learning, Pose and Pattern recognition.
His work on Artificial intelligence deals in particular with Human body, Image, Image segmentation, Articulated body pose estimation and Single image.
His Single image research is multidisciplinary, relying on both 2D to 3D conversion and Leverage.
His study in the field of Discriminative model also crosses realms of Set.
In his research, Active appearance model is intimately related to Representation, which falls under the overarching field of Pose.
His studies in Pattern recognition integrate themes in fields like Image resolution, Color constancy and Robustness.
His most cited work include:
- 2D Human Pose Estimation: New Benchmark and State of the Art Analysis (1296 citations)
- Keep It SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image (749 citations)
- Keep It SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image (749 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, Machine learning and Segmentation.
His work on Set expands to the thematically related Artificial intelligence.
Peter V. Gehler combines subjects such as Human body and Leverage with his study of Computer vision.
His biological study spans a wide range of topics, including Contextual image classification, Cognitive neuroscience of visual object recognition, Artificial neural network and Bilateral filter.
Peter V. Gehler has included themes like Perspective, Generative grammar, Inference and Graphics in his Machine learning study.
His work in the fields of Pose, such as 3D pose estimation, overlaps with other areas such as Set.
He most often published in these fields:
- Artificial intelligence (83.51%)
- Computer vision (37.11%)
- Pattern recognition (29.90%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (83.51%)
- Pattern recognition (29.90%)
- Machine learning (27.84%)
In recent papers he was focusing on the following fields of study:
Peter V. Gehler mainly focuses on Artificial intelligence, Pattern recognition, Machine learning, Deep learning and Segmentation.
Artificial intelligence connects with themes related to Set in his study.
As a member of one scientific family, he mostly works in the field of Pattern recognition, focusing on Robustness and, on occasion, Noise and Intersection.
His Machine learning research incorporates themes from Generative grammar and Modular design.
He has researched Deep learning in several fields, including Pixel, Image sensor and Pose.
His work in the fields of Image segmentation overlaps with other areas such as Network layer.
Between 2017 and 2021, his most popular works were:
- Neural Body Fitting: Unifying Deep Learning and Model Based Human Pose and Shape Estimation (260 citations)
- Deep Directional Statistics: Pose Estimation with Uncertainty Quantification (38 citations)
- Efficient 2D and 3D Facade Segmentation Using Auto-Context (32 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His scientific interests lie mostly in Artificial intelligence, Benchmark, Image segmentation, Segmentation and Pattern recognition.
Peter V. Gehler undertakes multidisciplinary studies into Artificial intelligence and Estimation in his work.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Deep learning and Pose.
His Feature extraction research includes themes of Point cloud and Data mining.
His work deals with themes such as Mixture model, Probabilistic logic, Uncertainty quantification and Directional statistics, which intersect with Robustness.
His research integrates issues of Representation, Parameterized complexity and Solid modeling in his study of Perspective.
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