Model-based recognition and motion tracking depends upon the ability to solve for projection and model parameters that will best fit a 3-D model to matching 2-D image features. This paper extends current methods of parameter solving to handle objects with arbitrary curved surfaces and with any number of internal parameters representing articulations, variable dimensions, or surface deformations. Numerical

David Lowe's influential and classic algorithm for tracking objects with known geometry is formulated with certain simplifying assumptions. A version implemented by Ishii et al. makes different simplifying assumptions. We formulate a full projective solution and apply the same algorithm (Newton's method). We report results of extensive testing of these three algorithms. We compute two image--space and six pose--space error metrics to quantify the effects of object pose, errors in initial solutions, and image noise levels. We consider several scenaria, from relatively unconstrained conditions to those that mirror real--world and real-- time constraints. The conclusion is that the full projective formulation makes the algorithm orders of magnitude more accurate and gives it super--exponential convergence properties with arguably better computation--time properties. This material is based on work supported by the Luso--American Foundation, Calouste Gulbenkian Foundation, JNICT, CAPES pro...

In this paper we review and compare several techniques for model--based pose recovery (extrinsic camera calibration) from monocular images. We classify the solutions reported in the literature as analytical perspective, affine and numerical perspective. We also present reformulations for two of the most important numerical perspective solutions: Lowe's algorithm and Phong--Horaud's algorithm. Our improvement to Lowe's algorithm consists of eliminating some simplifying assumptions on its projective equations. A careful experimental evaluation reveals that the resulting fully projective algorithm has superexponential convergence properties for a wide range of initial solutions and, under realistic usage conditions, it is up to an order of magnitude more accurate than the original formulation, with arguably better computation--time properties. Our extension to Phong--Horaud's algorithm is, to the best of our knowledge, the first method for independent orientation recovery that actually ex...