3-D object position estimation and recognition based on parameterized surfaces and multiple views

A new approach is introduced to 3-D parameterized object estimation and recognition. Though the theory is applicable for any parameterization, we use a model for which objects are approximated by patches of spheres, cylinders, and planes---primitive objects. These primitive surfaces are special cases of 3-D quadric surfaces. Primitive surface estimation is treated as parameter estimation using data patches in two or more noisy images taken by calibrated cameras in different locations and from different directions. Included is the case of a single moving camera. Though various techniques can be used to implement this nonlinear estimation, we discuss the use of gradient descent. Experiments are run and discussed for the case of a sphere of unknown location. It is shown that the estimation procedure can be viewed geometrically as a cross correlation of nonlinearly transformed image p a t d e s in two or more images. Approaches to object surface segmentation into primitive object surfaces, and primitive object-type recognition are briefly presented and discussed. The attractiveness of the approach is that maximum likelihood estimation and all the usual tools of statistical signal analysis can be brought o bear, the information extraction appears to be robust and computationally reasonable, the concepts are geometric and simple, and close to optimal accuracy should result.