J
Jan-Olof Eklundh
Researcher at Royal Institute of Technology
Publications - 100
Citations - 3173
Jan-Olof Eklundh is an academic researcher from Royal Institute of Technology. The author has contributed to research in topics: Active vision & Image segmentation. The author has an hindex of 28, co-authored 100 publications receiving 3093 citations. Previous affiliations of Jan-Olof Eklundh include University of Maryland, College Park.
Papers
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Book ChapterDOI
Detecting symmetry and symmetric constellations of features
Gareth Loy,Jan-Olof Eklundh +1 more
TL;DR: It is shown how symmetric pairs of features can be efficiently detected, how the symmetry bonding each pair is extracted and evaluated, and how these can be grouped into symmetric constellations that specify the dominant symmetries present in the image.
Book ChapterDOI
On the Significance of Real‐World Conditions for Material Classification
TL;DR: A first contribution of this paper is to further advance the state-of-the-art by applying Support Vector Machines to this problem and record the best results to date on the CUReT database.
Journal ArticleDOI
A Fast Computer Method for Matrix Transposing
TL;DR: A method is given for transposition of 2n×2n data matrices, larger than available high-speed storage, that should be stored on an external storage device, allowing direct access.
Proceedings ArticleDOI
Detecting and tracking moving objects from a mobile platform using a laser range scanner
M. Lindstrom,Jan-Olof Eklundh +1 more
TL;DR: An algorithm that identifies range readings in areas that was detected earlier as free is described, and is able to track a moving person walking around, while consuming only about 2% of the available processing power.
Journal ArticleDOI
Vision for robotic object manipulation in domestic settings
TL;DR: A vision system for robotic object manipulation tasks in natural, domestic environments and one important property is that the step from object recognition to pose estimation is completely automatic combining both appearance and geometric models.