This paper presents a framework for visual servoing that guarantees convergence to a visible goal from almost every initially visible configuration while maintaining full view of all the feature points along the way. The method applies to first and second order fully actuated plant models. The solution entails three components: a model for the "occlusion-free" configurations; a change of coordinates from image to model coordinates; and a navigation function for the model space. We present three example applications of the framework, along with experimental validation of its practical efficacy.

Introduction The idea for this Appendix arose from our perception of a frustrating situation faced by vision researchers. For example, one is interested in some aspect of the theory of perspective image formation such as the epipolar line. The interested party goes to the library to check out a book on projective geometry filled with hope that the necessary mathematical machinery will be directly at hand. These expectations are quickly dashed. Upon opening the book, the expectant reader finds the presentation dominated by endless observations about harmonic relations and a few chapters which explore the minutiae of Pappus' theorem. Finally, as a last cruel twist of irony, the book ends in triumph with a rather exhilarating discourse on the conic pencil. All of the material is presented in the form of theorems defined on points, lines and conics without the use of coordinates, except perhaps for a quick pause to define barycentric coordinates just to taunt the reader. Dejected, the vis

Model-based pose estimation techniques that match image and model triangles rquire large numbers of matching operations in realworld applications. We show that by using approximations to perspective, 2-D lookup tables can be built for each of the triangles of the models. An approximation called "weak perspective" has been applied previously to this problem; we consider two other perspective approximations: paraperspective and orthoperspective. These approximations produce lower errors for off-center image features than weak perspective.

In this paper a robust method to track a head in 3D using a static monocular camera is presented. Head pose is recovered by formulating the problem as a camera pose estimation problem. Several 3D feature points are acquired from the head prior to tracking and used as a model. Both artificial and natural occurring features can be used. Pose is estimated by solving a robust version of ”Perspective n Point ” problem (PnP). The proposed algorithm can handle self occlusions, outliers and recover from tracking failures. Results were validated by simulations and were compared to pose obtained using an accurate magnetic field 3D measuring device. Our system is not limited to tracking human heads and can be used to track animal heads as well. To demonstrate the applicability of our method, three types of heads were tracked (human, barn owl, chameleon) in a series of biological experiments. 1.

We describe a method for recovering the camera position and orientation parameters from a single mountain image. It is based on the alignment of the mountain skyline with a synthetic skyline generated from a digital elevation map. Image plane alignment of three image skyline feature points with three model feature points is first hypothesized and the position and orientation parameters for the hypothesis are computed by nonlinear least squares optimization.