Shaped-Based Recognition of 3D Objects from 2D Projections

Report No. ARL-TR-4006
Authors: Philip David; Daniel DeMenthon
Date/Pages: December 2006; 40 pages
Abstract: We present an object recognition algorithm that uses model and image line features to locate complex objects in high clutter environments. Corresponding line features are determined by a three-stage process. The first stage generates a large number of approximate pose hypotheses from correspondence of one or two lines in the model and image. Next, pose hypotheses from the previous stage are quickly evaluated and ranked by a comparison of local image neighborhoods to the corresponding local model neighborhoods. Fast nearest neighbor and range search algorithms are used to implement a distance measure that is unaffected by clutter and partial occlusion. The ranking of pose hypotheses is invariant to changes in image scale, orientation, and partially invariant to affine distortion. Finally, a robust pose estimation algorithm is applied for refinement and verification, starting from the few best approximate poses produced by the previous stages. Experiments on real images demonstrate robost recognition of partially occluded objects in very high clutter environments.
Distribution: Approved for public release
  Download Report ( 0.672 MBytes )
If you are visually impaired or need a physical copy of this report, please visit and contact DTIC.
 

Last Update / Reviewed: December 1, 2006