What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Computer vision
David Suter focuses on Artificial intelligence, Pattern recognition, Computer vision, Algorithm and Robustness.
His biological study spans a wide range of topics, including Machine learning and Filter.
The various areas that David Suter examines in his Pattern recognition study include Estimation theory, Estimator, Noise and Synthetic data.
Dimensionality reduction, Hausdorff distance, Pattern recognition, Contextual image classification and Motion analysis is closely connected to Similitude in his research, which is encompassed under the umbrella topic of Computer vision.
His work deals with themes such as Subspace topology and Missing data, Imputation, which intersect with Algorithm.
His work carried out in the field of Robustness brings together such families of science as Image processing, Total least squares and Heuristic.
His most cited work include:
- Joint Detection and Estimation of Multiple Objects From Image Observations (282 citations)
- Fast Supervised Hashing with Decision Trees for High-Dimensional Data (265 citations)
- As-Projective-As-Possible Image Stitching with Moving DLT (231 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Outlier.
His study ties his expertise on Machine learning together with the subject of Artificial intelligence.
His work is connected to Tracking, Image, Motion estimation, Motion and Eye tracking, as a part of Computer vision.
His Pattern recognition research incorporates themes from Facial recognition system and Subspace topology.
His Algorithm research is multidisciplinary, relying on both Sampling, Mathematical optimization and Geometric modeling.
His study in Outlier is interdisciplinary in nature, drawing from both Data point and Real image.
He most often published in these fields:
- Artificial intelligence (71.16%)
- Computer vision (40.45%)
- Pattern recognition (32.58%)
What were the highlights of his more recent work (between 2012-2021)?
- Artificial intelligence (71.16%)
- Algorithm (20.97%)
- Outlier (11.99%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Algorithm, Outlier, Geometric modeling and Real image.
His Artificial intelligence research integrates issues from Machine learning, Computer vision and Pattern recognition.
The Pattern recognition study combines topics in areas such as Facial recognition system and Feature.
His Algorithm study incorporates themes from Sampling, Structure, Epipolar geometry and Rate of convergence.
The Robust statistics research David Suter does as part of his general Outlier study is frequently linked to other disciplines of science, such as Zero, therefore creating a link between diverse domains of science.
David Suter works mostly in the field of Geometric modeling, limiting it down to concerns involving Computational complexity theory and, occasionally, Reinforcement learning.
Between 2012 and 2021, his most popular works were:
- Fast Supervised Hashing with Decision Trees for High-Dimensional Data (265 citations)
- As-Projective-As-Possible Image Stitching with Moving DLT (231 citations)
- A General Two-Step Approach to Learning-Based Hashing (162 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Computer vision
Artificial intelligence, Mathematical optimization, Pattern recognition, Robustness and Context are his primary areas of study.
His research in Artificial intelligence intersects with topics in Hypergraph and Computer vision.
His studies deal with areas such as Algorithm design and Mode as well as Computer vision.
His Mathematical optimization research includes themes of Point cloud, Contrast and Regularization.
His work on Feature extraction and Discriminative model as part of general Pattern recognition research is frequently linked to Hamming space and Filter bank, thereby connecting diverse disciplines of science.
His Robustness study combines topics from a wide range of disciplines, such as Penalty method, Local convergence, Brute-force search and Minification.
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