Artificial intelligence, Computer vision, Optical flow, Motion estimation and Algorithm are his primary areas of study. Artificial intelligence and Machine learning are commonly linked in his work. His Computer vision study incorporates themes from Representation, Facial expression and Affine transformation.
The study incorporates disciplines such as Flow, Iterative reconstruction, Robustness and Image processing in addition to Optical flow. His studies deal with areas such as Particle filter, Probabilistic logic and Classification of discontinuities as well as Motion estimation. His Algorithm study combines topics in areas such as Kalman filter, Outlier, Rendering and Pattern recognition.
His primary scientific interests are in Artificial intelligence, Computer vision, Optical flow, Pattern recognition and Motion. Many of his studies involve connections with topics such as Machine learning and Artificial intelligence. His study in Segmentation, Image, Image segmentation, Monocular and Motion field is carried out as part of his Computer vision studies.
His Optical flow research is multidisciplinary, incorporating elements of Flow, Algorithm, Pixel and Robustness. His work focuses on many connections between Pattern recognition and other disciplines, such as Representation, that overlap with his field of interest in Polygon mesh. His research combines Affine transformation and Motion estimation.
Michael J. Black focuses on Artificial intelligence, Computer vision, Artificial neural network, Motion and Motion capture. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Code and Pattern recognition. His research integrates issues of Optical flow, Representation and Task in his study of Pattern recognition.
In his work, Gesture is strongly intertwined with Deep learning, which is a subfield of Computer vision. The various areas that Michael J. Black examines in his Motion study include Polygon mesh, Inference, Motion analysis and Body shape. His Motion capture research is multidisciplinary, relying on both 3D pose estimation, Cognitive psychology and Inertial measurement unit.
His primary areas of investigation include Artificial intelligence, Computer vision, Artificial neural network, Ground truth and Face. His Artificial intelligence research includes elements of Polygon mesh and Code. His Motion capture, Monocular, Pixel, Image and Inertial measurement unit investigations are all subjects of Computer vision research.
Michael J. Black has included themes like Computer engineering, Eye tracking, Optical flow, Robot and Robustness in his Artificial neural network study. His Optical flow study incorporates themes from Image resolution, Pyramid and Unsupervised learning, Pattern recognition. In Face, Michael J. Black works on issues like RGB color model, which are connected to Facial expression.
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A Database and Evaluation Methodology for Optical Flow
Simon Baker;Daniel Scharstein;J. P. Lewis;Stefan Roth.
International Journal of Computer Vision (2011)
The Robust Estimation of Multiple Motions
Michael J. Black;P. Anandan.
Computer Vision and Image Understanding (1996)
Robust anisotropic diffusion
M.J. Black;G. Sapiro;D.H. Marimont;D. Heeger.
IEEE Transactions on Image Processing (1998)
Secrets of optical flow estimation and their principles
Deqing Sun;Stefan Roth;Michael J. Black.
computer vision and pattern recognition (2010)
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
Michael J. Black;Allan D. Jepson.
International Journal of Computer Vision (1998)
Fields of Experts: a framework for learning image priors
S. Roth;M.J. Black.
computer vision and pattern recognition (2005)
A naturalistic open source movie for optical flow evaluation
Daniel J. Butler;Jonas Wulff;Garrett B. Stanley;Michael J. Black.
european conference on computer vision (2012)
HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion
Leonid Sigal;Alexandru O. Balan;Michael J. Black.
International Journal of Computer Vision (2010)
Stochastic Tracking of 3D Human Figures Using 2D Image Motion
Hedvig Sidenbladh;Michael J. Black;David J. Fleet.
european conference on computer vision (2000)
On the unification of line processes, outlier rejection, and robust statistics with applications in early vision
Michael J. Black;Anand Rangarajan.
International Journal of Computer Vision (1996)
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