In this paper, we present a complete system for the recognition and localization of a three-dimensional (3-D) model from a sequence of monocular images with known motion. The originality of this system is twofold. First, it uses a purely 3-D approach, starting from the 3-D reconstruction of the scene and ending by the 3-D matching of the model. Second, unlike most monocular systems, we do not use token tracking to match successive images. Rather, subpixel contour matching is used to recover more precisely complete 3-D contours. In contrast with the token tracking approaches, which yield a representation of the 3-D scene based on disconnected segments or points, this approach provides us with a denser and higher level representation of the scene. The reconstructed contours are fused along successive images using a simple result derived from the Kalman filter theory. The fusion process increases the localization precision and the robustness of the 3-D reconstruction. Finally, corners are extracted from the 3-D contours. They are used to generate hypotheses of the model position, using a hypothesize-and-verify algorithm that is described in detail. This algorithm yields a robust recognition and precise localization of the model in the scene. Results are presented on infrared image sequences with different resolutions, demonstrating the precision of the localization as well as the robustness and the low computational complexity of the algorithms.

This paper introduces a new general purpose algorithm that allows the optimal geometric match between contours to be determined, that is the transformation yielding a minimal deformation is obtained. The algorithm relies only on the geometric properties of the contours and does not call for any other constraint, so that it is particularly suitable when no parameterization of title deformation is available or desirable. Contour deformation is explicitly incorporated in the computation, allowing for a thorough use of all geometric information available. Moreover, no discretization is involved in the computation, resulting in two main advantages: first, the algorithm is robust to differences in the segmentation of contours and allows the matching of polygonal approximations of contours with very little loss of precision, second, subpixel precision matching can be achieved.>

This paper presents a subpixel contour matching algorithm using a novel dynamic programming scheme. Unlike classical dynamic programming methods, where a discrete path is searched for across a graph, our approach allows the optimal continuous path to be determined. The matching is obtained as a piecewise parametric function, and no discretization is involved, nor any parameterized deformation assumed. As a side result, a measure of the deformation of the matched contours is obtained, yielding information on the quality of the match. The algorithm has been tested with different types of images, demonstrating its ability to deal with chains of contour segments as well as chains of contour edges, since the discretization of the contours does not limit the precision of the matches anymore.>