What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
His primary areas of investigation include Artificial intelligence, Computer vision, Iterative reconstruction, 3D reconstruction and Image.
His research in Artificial intelligence tackles topics such as Surface reconstruction which are related to areas like Image segmentation.
Within one scientific family, Jan-Michael Frahm focuses on topics pertaining to Computer graphics under Computer vision, and may sometimes address concerns connected to Graphics processing unit and Camera auto-calibration.
The study incorporates disciplines such as Image resolution, Image processing, Pixel, Frame rate and Image registration in addition to Iterative reconstruction.
His research in Image registration intersects with topics in Feature extraction and Computer engineering.
His Image research is multidisciplinary, incorporating perspectives in Completeness and Cluster analysis.
His most cited work include:
- Structure-from-Motion Revisited (1070 citations)
- Detailed Real-Time Urban 3D Reconstruction from Video (650 citations)
- Pixelwise View Selection for Unstructured Multi-View Stereo (497 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Computer vision, 3D reconstruction, Image and Pixel.
His research brings together the fields of Pattern recognition and Artificial intelligence.
His work on Computer vision is being expanded to include thematically relevant topics such as Computer graphics.
His studies examine the connections between Pixel and genetics, as well as such issues in Algorithm, with regards to Vanishing point.
His Structure from motion research includes themes of Graphics, Feature and Bundle adjustment.
His Robustness study integrates concerns from other disciplines, such as Machine learning and RANSAC.
He most often published in these fields:
- Artificial intelligence (77.23%)
- Computer vision (64.36%)
- 3D reconstruction (15.84%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (77.23%)
- Computer vision (64.36%)
- Inference (3.47%)
In recent papers he was focusing on the following fields of study:
Jan-Michael Frahm focuses on Artificial intelligence, Computer vision, Inference, Convolutional neural network and Machine learning.
His Artificial intelligence research focuses on Key and how it relates to Inference attack.
His study in Computer vision is interdisciplinary in nature, drawing from both Representation and Surface.
The various areas that Jan-Michael Frahm examines in his Surface study include 3D reconstruction, Preprocessor and Selection.
In his study, which falls under the umbrella issue of Convolutional neural network, Geodesic grid, Convolution, Icosahedral symmetry, Kernel and Scaling is strongly linked to Spherical image.
His Machine learning research focuses on Keystroke logging and how it connects with Synthetic data.
Between 2018 and 2021, his most popular works were:
- Recurrent Neural Network for (Un-)Supervised Learning of Monocular Video Visual Odometry and Depth (46 citations)
- Recurrent Neural Network for (Un-)supervised Learning of Monocular VideoVisual Odometry and Depth (32 citations)
- Re-Thinking CNN Frameworks for Time-Sensitive Autonomous-Driving Applications: Addressing an Industrial Challenge (17 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Algorithm
Jan-Michael Frahm spends much of his time researching Artificial intelligence, Computer vision, Segmentation, Artificial neural network and Convolution.
His study in Deep learning, Visual odometry, Depth map, Convolutional neural network and Spherical image falls under the purview of Artificial intelligence.
His work in Computer vision addresses issues such as Odometry, which are connected to fields such as Monocular.
His Segmentation study combines topics in areas such as Signed distance function, Feature extraction, Geodesic grid and Leverage.
Jan-Michael Frahm has included themes like Computer architecture, Inference and Composite image filter in his Artificial neural network study.
His Convolution research includes elements of Map projection, Representation and Projection.
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