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Gyuri Dorkó
Researcher at French Institute for Research in Computer Science and Automation
Publications - 11
Citations - 884
Gyuri Dorkó is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: Feature selection & Scale-invariant feature transform. The author has an hindex of 10, co-authored 11 publications receiving 852 citations. Previous affiliations of Gyuri Dorkó include Technische Universität Darmstadt.
Papers
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Book ChapterDOI
The 2005 PASCAL visual object classes challenge
Mark Everingham,Andrew Zisserman,Christopher Williams,Luc Van Gool,Moray Allan,Christopher M. Bishop,Olivier Chapelle,Navneet Dalal,Thomas Deselaers,Gyuri Dorkó,Stefan Duffner,J Eichhorn,Jason Farquhar,Mario Fritz,Christophe Garcia,Tom Griffiths,Frédéric Jurie,Daniel Keysers,Markus Koskela,Jorma Laaksonen,Diane Larlus,Bastian Leibe,Hongying Meng,Hermann Ney,Bernt Schiele,Cordelia Schmid,Edgar Seemann,John Shawe-Taylor,Amos Storkey,Sandor Szedmak,Bill Triggs,Ilkay Ulusoy,Ville Viitaniemi,Jianguo Zhang +33 more
TL;DR: The PASCAL Visual Object Classes Challenge (PASCALVOC) as mentioned in this paper was held from February to March 2005 to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects).
Object Class Recognition Using Discriminative Local Features
Gyuri Dorkó,Cordelia Schmid +1 more
TL;DR: A scale-invariant feature selection method that learns to recognize and detect object classes from images of natural scenes that uses local regions to realize robust and sparse part and texture selection invariant to changes in scale, orientation and affine deformation.
Book ChapterDOI
Sliding-Windows for Rapid Object Class Localization: A Parallel Technique
TL;DR: A fast object class localization framework implemented on a data parallel architecture currently available in recent computers, and using recent techniques to program the Graphics Processing Unit (GPU) allow this method to scale up to the latest, as well as to future improvements of the hardware.
Journal ArticleDOI
Learning to Recognize Objects with Little Supervision
TL;DR: By adopting sparse kernel methods, Bayesian learning techniques and data association with constraints, the proposed model identifies the most relevant sets of local features for recognizing object classes, achieves performance comparable to the fully supervised setting, and obtains excellent results for image classification.
Proceedings ArticleDOI
Combining Regions and Patches for Object Class Localization
TL;DR: A method for object class detection and localization which combines regions generated by image segmentation with local patches, and applies Region-based Context Features in a semi-supervised learning framework for object Detection and localization.