Recognizing parameterized three-dimensional objects
Robert R. Goldberg · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1994
Complex object models require multiple components affixed to each other in specific and variable geometric paths. This paper expands upon earlier research to present an unified approach for relating components' coordinate systems to each other in the same model. Particularly, we show that rather complex relationships such as ball joints and geometric transformations about arbitrary axes are no more complicated than describing the model base in terms of the camera coordinate system. These require only simple rotations and translations about the major axes. This modeling approach was next integrated with a verification module of a model based vision system. We recovered from a single 2D image the original model and camera parameters that would align the projected model edges with the image segments by solving a nonlinear least squares system. A specific example of the theory is implemented. A lamp head is seceded to its base by a ball joint with three parameters of rotational freedom. From a wide range of initial guess error, the numerical system converged to the correct set of model and camera parameters. Thus, the theory of parameterized affixments and the numerical implementation to obtain these values from 2D images will aid in associated recognition tasks and in real-time tracking of complex conglomerate objects.