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Multi-scale Features for Detection and Segmentation of Rocks in Mars Images
H. Dunlop, D.R. Thompson, and D. Wettergreen
IEEE Conference on Computer Vision and Pattern Recognition, June, 2007.

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Abstract

Geologists and planetary scientists will benefit from methods for accurate segmentation of rocks in natural scenes. However, rocks are poorly suited for current visual segmentation techniques - they exhibit diverse morphologies and have no uniform property to distinguish them from background soil. We address this challenge with a novel detection and segmentation method incorporating features from multiple scales. These features include local attributes such as texture, object attributes such as shading and two-dimensional shape, and scene attributes such as the direction of illumination. Our method uses a superpixel segmentation followed by region-merging to search for the most probable groups of superpixels. A learned model of rock appearances identifies whole rocks by scoring candidate superpixel groupings. We evaluate our method's performance on representative images from the Mars Exploration Rover catalog.

Notes

Sponsor: NASA
Grant ID: NNG0-4GB66G and NAG5-12890

Associated center: FRC
Associated project: Science Autonomy

Number of pages: 7

Text Reference

H. Dunlop, D.R. Thompson, and D. Wettergreen, "Multi-scale Features for Detection and Segmentation of Rocks in Mars Images," IEEE Conference on Computer Vision and Pattern Recognition, June, 2007.

BibTeX Reference

@inproceedings{Dunlop_2007_5785,
   author = "Heather Dunlop and David R Thompson and David Wettergreen",
   title = "Multi-scale Features for Detection and Segmentation of Rocks in Mars Images",
   booktitle = "IEEE Conference on Computer Vision and Pattern Recognition",
   month = "June",
   year = "2007"
}