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D Koubaroulis and Ji{\v r}{\' \i} Matas and Josef Kittler
ABSTRACT
In this paper, a new image set, called the Surrey Object Image Library (SOIL-47) is introduced, on which the performance of two colour-based object recognition methods is evaluated. The data was collected specifically for testing colour-based recognition algorithm and is publicly available. In the conducted experiments on SOIL-47, we evaluate two recognition algoritms; the Multimodal Neighbourhood Signature (MNS) approach and a method based on a Attributed Relational Graph (ARG). The MNS approach represents object appearance by measurements computed from image neighbourhoods with a multimodal colour density function. The ARG approach computes a graph of affine invariant measurements of the colour and shape of segmented image regions. Using only a single model image of each of the 47 objects, MNS performed well even for extreme test views close to degrees. The ARG method assumes a locally planar surface, therefore a second experiment was conducted on a subset of box-like objects of SOIL-47. MNS performance was fairly stable, outperforming ARG for most viewing angles.. Note, that this is the first systematic test of MNS with controlled 3D viewpoint change. 
ECVision indexed and annotated bibliography of cognitive computer vision publications
This bibliography was created by Hilary Buxton and Benoit Gaillard, University of Sussex, as part of ECVision Specific Action 8-1
The complete text version of this BibTeX file is available here: ECVision_bibliography.bib
Evaluating Colour-Based Object Recognition Algorithms on the {SOIL}-47 DatabaseSite generated on Friday, 06 January 2006