Making specific features less discriminative to improve point-based 3D object recognition

Edward Hsiao, Alvaro Collet Romea, and Martial Hebert
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July, 2010.


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Abstract
We present a framework that retains ambiguity in feature matching to increase the performance of 3D object recognition systems. Whereas previous systems removed ambiguous correspondences during matching, we show that ambiguity should be resolved during hypothesis testing and not at the matching phase. To preserve ambiguity during matching, we vector quantize and match model features in a hierarchical manner. This matching technique allows our system to be more robust to the distribution of model descriptors in feature space. We also show that we can address recognition under arbitrary viewpoint by using our framework to facilitate matching of additional features extracted from affine transformed model images. The evaluation of our algorithms in 3D object recognition is demonstrated on a difficult dataset of 620 images.

Notes

Text Reference
Edward Hsiao, Alvaro Collet Romea, and Martial Hebert, "Making specific features less discriminative to improve point-based 3D object recognition," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July, 2010.

BibTeX Reference
@inproceedings{Hsiao_2010_6572,
   author = "Edward Hsiao and Alvaro {Collet Romea} and Martial Hebert",
   title = "Making specific features less discriminative to improve point-based 3D object recognition",
   booktitle = "IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
   month = "July",
   year = "2010",
}