In this paper we demonstrate how Interval Analysis and Constraint Logic Programming can be used to obtain an accurate geometric model of a scene that rigorously takes into account the propagation of data errors and roundoff. Image points are represented as small rectangles: As a result, the output of the n-views triangulation is not a single point in space, but a polyhedron that contains all the possible solutions. Interval Analysis is used to bound this polyhedron with a box. Geometrical constraints such as orthogonality, parallelism, and coplanarity are subsequently enforced in order to reduce the size of those boxes, using Constraint Logic Programming. Experiments with real calibrated images illustrate the approach. 1.

In this report we deal with the correct formulation of a special extended interval arithmetic in the context of interval Newton like methods. We first demonstrate some of the problems arising from selected older definitions. Then we investigate the basic aim, concept, and properties important for defining a correct extended interval division. Finally, we give a proper way for defining the extended interval operations needed in our special context, and we prove their inclusion isotonicity. Additionally, we give some sample applications. We conclude with two updated implementations of our extended interval operations in the toolbox environments [2] and [3].

In this paper we discuss how Interval Analysis can be used to solve some problems in Computer Vision, namely autocalibration and triangulation. The crucial property of Interval Analysis is its ability to rigorously bound the range of a function over a given domain. This allows to propagate input errors with guaranteed results (used in multi-views triangulation) and to search for solution in non-linear minimisation problems with provably correct branch-and-bound algorithms (used in autocalibration). Experiments with real calibrated images illustrate the interval approach. Categories and Subject Descriptors (according to ACM