2022 - Research.com Computer Science in Belgium Leader Award
Tinne Tuytelaars mainly focuses on Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Cognitive neuroscience of visual object recognition. His Artificial intelligence study frequently links to other fields, such as Machine learning. The concepts of his Computer vision study are interwoven with issues in Invariant and Robustness.
He has researched Robustness in several fields, including Image database and Stereo matching. Tinne Tuytelaars studied Cognitive neuroscience of visual object recognition and Visual Word that intersect with Content-based image retrieval and Affine transformation. His Interest point detection research is multidisciplinary, relying on both Point detector and Scale-invariant feature transform.
His primary areas of study are Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Object. His works in Object detection, Image, Cognitive neuroscience of visual object recognition, Deep learning and Feature extraction are all subjects of inquiry into Artificial intelligence. His work carried out in the field of Object detection brings together such families of science as Object-class detection and Viola–Jones object detection framework.
He interconnects Robot and Invariant in the investigation of issues within Computer vision. His study in the field of Discriminative model is also linked to topics like Set. His research in Machine learning intersects with topics in Domain, Representation, Forgetting, Benchmark and Set.
His primary areas of investigation include Artificial intelligence, Machine learning, Deep learning, Pattern recognition and Computer vision. His Artificial intelligence study frequently links to related topics such as Forgetting. His Machine learning research incorporates elements of Matching, Feature extraction and Image retrieval.
His research integrates issues of Contextual image classification, Pose and Face in his study of Pattern recognition. Tinne Tuytelaars integrates several fields in his works, including Computer vision and Memory map. His work in Image resolution addresses issues such as Transfer of learning, which are connected to fields such as Robustness and Convolutional neural network.
Artificial intelligence, Machine learning, Forgetting, Deep learning and Artificial neural network are his primary areas of study. His research is interdisciplinary, bridging the disciplines of Computer vision and Artificial intelligence. His Computer vision research includes themes of Key, Bandwidth and Reinforcement learning.
His work deals with themes such as Matching, Feature extraction and State, which intersect with Machine learning. In his study, which falls under the umbrella issue of Forgetting, Robustness, Data stream, Feature learning, Data stream mining and Concept drift is strongly linked to Incremental learning. As a part of the same scientific family, Tinne Tuytelaars mostly works in the field of Deep learning, focusing on Human–computer interaction and, on occasion, Embedded computer vision, Image processing and Facial recognition system.
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SURF: speeded up robust features
Herbert Bay;Tinne Tuytelaars;Luc Van Gool.
european conference on computer vision (2006)
Speeded-Up Robust Features (SURF)
Herbert Bay;Andreas Ess;Tinne Tuytelaars;Luc Van Gool.
Computer Vision and Image Understanding (2008)
A Comparison of Affine Region Detectors
K. Mikolajczyk;T. Tuytelaars;C. Schmid;A. Zisserman.
International Journal of Computer Vision (2005)
Local Invariant Feature Detectors: A Survey
Tinne Tuytelaars;Krystian Mikolajczyk.
An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector
Geert Willems;Tinne Tuytelaars;Luc Gool.
european conference on computer vision (2008)
Unsupervised Visual Domain Adaptation Using Subspace Alignment
Basura Fernando;Amaury Habrard;Marc Sebban;Tinne Tuytelaars.
international conference on computer vision (2013)
Matching Widely Separated Views Based on Affine Invariant Regions
Tinne Tuytelaars;Luc Van Gool.
International Journal of Computer Vision (2004)
Wide Baseline Stereo Matching based on Local, Affinely Invariant Regions
Tinne Tuytelaars;Luc J. Van Gool.
british machine vision conference (2000)
Dynamic Filter Networks
Xu Jia;Bert De Brabandere;Tinne Tuytelaars;Luc Van Gool.
neural information processing systems (2016)
Object detection by contour segment networks
Vittorio Ferrari;Tinne Tuytelaars;Luc Van Gool.
european conference on computer vision (2006)
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