3-d space location and orientation parameter estimation of lambertian spheres and cylinders from a single 2-d image by fitting lines and ellipses to thresholded data

An approach to object location and orientation estimation is discussed in which objects in 3-D space are approximated by chunks of spheres, cylinders, and planes. The surface-shape parameters of these chunks of primitive subobjects are estimated in real time from a single 2-D image assuming a Lambertian reflection model. This processing is realized by partitioning an image into small square windows and processing the windows in parallel. It is assumed that a small window views a portion of one of the spherical, cylindrical or planar chunks. The paper applies standard statistical estimators in new ways to the estimation of the 3-D shape parameters for spherical and cylindrical surfaces. Linear regression and scatter matrix eigenvalue analysis techniques are used here. The algorithms are computationally simple yet are robust and can handle noisy highly variable data.