The Robotics Institute

Carnegie Mellon Robotics Institute

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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.

Keywords

Pattern Recognition, Segmentation, Object Detection, Science Autonomy

Notes

Sponsor: NASA

Grant ID: NNG0-4GB66G and NAG5-12890

Associated Center(s) / Consortia: Field Robotics Center

Associated Project(s): Science Autonomy

Number of pages: 7

Text Reference

Heather Dunlop, David R. Thompson, and David 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",
}

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