What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Mean-shift and Image segmentation.
His Artificial intelligence research is multidisciplinary, incorporating elements of Covariance matrix and Covariance.
His research in Computer vision intersects with topics in Discriminative model and Pattern recognition.
His work focuses on many connections between Pattern recognition and other disciplines, such as Estimation theory, that overlap with his field of interest in Estimator and Linear subspace.
Peter Meer has researched Mean-shift in several fields, including Nonparametric statistics and Kernel.
His Kernel study combines topics in areas such as Smoothing and Feature vector.
His most cited work include:
- Mean shift: a robust approach toward feature space analysis (9295 citations)
- Kernel-based object tracking (4322 citations)
- Real-time tracking of non-rigid objects using mean shift (2814 citations)
What are the main themes of his work throughout his whole career to date?
Peter Meer mostly deals with Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Estimator.
Much of his study explores Artificial intelligence relationship to Estimation theory.
His work carried out in the field of Computer vision brings together such families of science as Robust regression and Data point.
Peter Meer studied Algorithm and Heteroscedasticity that intersect with Errors-in-variables models.
The study incorporates disciplines such as Robust statistics, Graph and Cluster analysis in addition to Pattern recognition.
His Estimator research is multidisciplinary, incorporating perspectives in Covariance matrix, Mathematical optimization, Outlier and Projection.
He most often published in these fields:
- Artificial intelligence (76.51%)
- Computer vision (46.31%)
- Algorithm (31.54%)
What were the highlights of his more recent work (between 2006-2020)?
- Artificial intelligence (76.51%)
- Pattern recognition (28.19%)
- Computer vision (46.31%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Computer vision, Algorithm and Robust statistics.
As part of his studies on Artificial intelligence, he frequently links adjacent subjects like Estimator.
His research integrates issues of Space, Pixel, Graph and Cluster analysis in his study of Pattern recognition.
His Tracking, Active contour model, Texton and Noise study in the realm of Computer vision interacts with subjects such as Breast cancer.
Peter Meer combines subjects such as Point cloud, Mathematical optimization and Manifold with his study of Algorithm.
His Mean-shift study also includes
- Fundamental matrix which is related to area like Differential geometry, Euclidean geometry and Feature vector,
- Grassmannian that connect with fields like Smoothing.
Between 2006 and 2020, his most popular works were:
- Pedestrian Detection via Classification on Riemannian Manifolds (764 citations)
- Human Detection via Classification on Riemannian Manifolds (427 citations)
- Robust and fast collaborative tracking with two stage sparse optimization (211 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Computer vision
His primary areas of study are Artificial intelligence, Computer vision, Pattern recognition, Object detection and Vector space.
Peter Meer regularly links together related areas like Covariance matrix in his Artificial intelligence studies.
His Computer vision research is multidisciplinary, relying on both Sparse approximation, Discriminative model and Lie algebra.
His Pattern recognition study incorporates themes from CURE data clustering algorithm, Clustering high-dimensional data, Cluster analysis and Fuzzy clustering.
The various areas that Peter Meer examines in his Vector space study include Riemannian manifold, Covariance, Symmetric matrix and Riemannian geometry.
His Segmentation research is multidisciplinary, incorporating elements of Object and Mean-shift.
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