Artificial intelligence, Computer vision, Object detection, Pose and Covariance are his primary areas of study. His research on Artificial intelligence often connects related areas such as Pattern recognition. He focuses mostly in the field of Computer vision, narrowing it down to matters related to Robustness and, in some cases, Mean-shift, Morphological filtering, Tracking system and Shadow.
Oncel Tuzel interconnects Lie group, Feature and Voxel in the investigation of issues within Object detection. His work is dedicated to discovering how Pose, Key are connected with Machine learning and other disciplines. His Covariance research is multidisciplinary, incorporating elements of Riemannian manifold, Vector space and Covariance matrix.
Oncel Tuzel mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Image and Pixel. His research in Artificial intelligence focuses on subjects like Machine learning, which are connected to Feature extraction. His Computer vision study integrates concerns from other disciplines, such as Sequence and Boundary.
His Pattern recognition research includes elements of Covariance and Feature. His Covariance study incorporates themes from Riemannian manifold and Covariance matrix. As part of one scientific family, Oncel Tuzel deals mainly with the area of Pixel, narrowing it down to issues related to the Recurrent neural network, and often Minimum bounding box.
His main research concerns Artificial intelligence, Algorithm, Sample, Speech recognition and Artificial neural network. His Artificial intelligence research includes themes of Margin, Machine learning and Time series. His work carried out in the field of Algorithm brings together such families of science as Nonlinear conjugate gradient method and Stochastic gradient descent.
His studies in Speech recognition integrate themes in fields like Word and Metric. His studies deal with areas such as Matching, Optimization problem, Empirical risk minimization and Empirical distribution function as well as Artificial neural network. His study in Object detection is interdisciplinary in nature, drawing from both Ranking, Point cloud, Feature extraction and Contextual image classification.
His primary areas of investigation include Artificial intelligence, Machine learning, Object detection, Artificial neural network and Class. In his papers, Oncel Tuzel integrates diverse fields, such as Artificial intelligence and Task. His study looks at the intersection of Feature extraction and topics like Point cloud with RGB color model and Leverage.
His Adversarial system study combines topics from a wide range of disciplines, such as Adversary, Image and Robustness. His Scaling study combines topics in areas such as Algorithm, Quantization, Inference and Mathematical proof. The various areas that he examines in his Feature engineering study include Voxel, Feature, Computer vision, Margin and Minimum bounding box.
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Region Covariance : A Fast Descriptor for Detection and Classification
Oncel Tuzel;Fatih Porikli;Peter Meer.
Lecture Notes in Computer Science (2006)
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
Yin Zhou;Oncel Tuzel.
computer vision and pattern recognition (2018)
Learning from Simulated and Unsupervised Images through Adversarial Training
Ashish Shrivastava;Tomas Pfister;Oncel Tuzel;Joshua Susskind.
computer vision and pattern recognition (2017)
Coupled Generative Adversarial Networks
Ming-Yu Liu;Oncel Tuzel.
neural information processing systems (2016)
Pedestrian Detection via Classification on Riemannian Manifolds
O. Tuzel;F. Porikli;P. Meer.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2008)
Entropy rate superpixel segmentation
Ming-Yu Liu;Oncel Tuzel;Srikumar Ramalingam;Rama Chellappa.
computer vision and pattern recognition (2011)
Covariance Tracking using Model Update Based on Lie Algebra
F. Porikli;O. Tuzel;P. Meer.
computer vision and pattern recognition (2006)
Human Detection via Classification on Riemannian Manifolds
O. Tuzel;F. Porikli;P. Meer.
computer vision and pattern recognition (2007)
A Multi-stream Bi-directional Recurrent Neural Network for Fine-Grained Action Detection
Bharat Singh;Tim K. Marks;Michael Jones;Oncel Tuzel.
computer vision and pattern recognition (2016)
Fast directional chamfer matching
Ming-Yu Liu;Oncel Tuzel;Ashok Veeraraghavan;Rama Chellappa.
computer vision and pattern recognition (2010)
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