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Robust Local Features and their Application in Self-Calibration and Object Recognition on Embedded Systems

12 years 3 months ago
Robust Local Features and their Application in Self-Calibration and Object Recognition on Embedded Systems
In recent years many powerful Computer Vision algorithms have been invented, making automatic or semiautomatic solutions to many popular vision tasks, such as visual object recognition or camera calibration, possible. On the other hand embedded vision platforms and solutions such as smart cameras have successfully emerged, however, only offering limited computational and memory resources. The first contribution of this paper is the investigation of a set of robust local feature detectors and descriptors for application on embedded systems. We briefly describe the methods involved, i.e. the DoG (Difference of Gaussian) and MSER (Maximally Stable Extremal Regions) detector as well as the PCA-SIFT descriptor, and discuss their suitability for smart systems and their qualification for given tasks. The second contribution of this work is the experimental evaluation of these methods on two challenging tasks, namely fully embedded object recognition on a moderate size database and on the tas...
Clemens Arth, Christian Leistner, Horst Bischof
Added 12 Oct 2009
Updated 08 Jul 2010
Type Conference
Year 2007
Where CVPR
Authors Clemens Arth, Christian Leistner, Horst Bischof
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