Multi-scale Features for Detection and Segmentation of Rocks in Mars Images
Project Details
Background: Geologists and planetary scientists can 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.
Solution: We addressed this challenge with a novel detection and segmentation method incorporating features from multiple scales. These features included 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 used a superpixel segmentation followed by region-merging to search for the most probable groups of superpixels. A learned model of rock appearances identified whole rocks by scoring candidate superpixel groupings. We evaluated our method’s performance on representative images from the Mars Exploration Rover catalog.
Alternative Applications: Many natural objects and biological structures exhibit similarly diverse morphologies and may also benefit from an integrated detection and segmentation routine.