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 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.
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.
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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Johannes L. Schonberger;Jan-Michael Frahm.
computer vision and pattern recognition (2016)
Detailed Real-Time Urban 3D Reconstruction from Video
M. Pollefeys;D. Nistér;J. M. Frahm;A. Akbarzadeh.
International Journal of Computer Vision (2008)
Pixelwise View Selection for Unstructured Multi-View Stereo
Johannes L. Schönberger;Enliang Zheng;Jan Michael Frahm;Marc Pollefeys;Marc Pollefeys.
european conference on computer vision (2016)
Building Rome on a cloudless day
Jan-Michael Frahm;Pierre Fite-Georgel;David Gallup;Tim Johnson.
european conference on computer vision (2010)
From structure-from-motion point clouds to fast location recognition
Arnold Irschara;Christopher Zach;Jan-Michael Frahm;Horst Bischof.
computer vision and pattern recognition (2009)
A Comparative Analysis of RANSAC Techniques Leading to Adaptive Real-Time Random Sample Consensus
Rahul Raguram;Jan-Michael Frahm;Marc Pollefeys.
european conference on computer vision (2008)
GPU-based Video Feature Tracking And Matching
Sudipta N. Sinha;Jan-Michael Frahm;Marc Pollefeys;Yakup Genc.
USAC: A Universal Framework for Random Sample Consensus
R. Raguram;O. Chum;M. Pollefeys;J. Matas.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2013)
Comparative Evaluation of Binary Features
Jared Heinly;Enrique Dunn;Jan-Michael Frahm.
european conference on computer vision (2012)
Real-Time Visibility-Based Fusion of Depth Maps
P. Merrell;A. Akbarzadeh;Liang Wang;P. Mordohai.
international conference on computer vision (2007)
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