Extrinsically calibrating a multi camera system from scene images is in general a very
difficult problem. One promising approach uses pairwise relative poses as input. As a limited …

Given a sequence of 2D images, such as a video or a set of unorganized pictures, what can
be inferred about the 3D scene? Where was the camera at each picture? What were the …

We present a new method for solving the problem of camera pose and calibration from a
limited number of correspondences between noisy 2D and 3D features. We show that the …

Detecting image pairs with a common field of view is an important prerequisite for many
computer vision tasks. Typically, common local features are used as a criterion for identifying …

We propose a novel minimum uncertainty approach to relative pose selection for multi-
camera self-calibration. We show how this discrete global optimization problem can be …

Les travaux présentés ici concernent le domaine de la localisation de véhicule par vision
artificielle. Dans ce contexte, la trajectoire d'une caméra et la structure3D de la scène filmée …

In this study we use statistical methods for assessing the quality of camera self-calibration of
a stereo vision system, considering accuracy, reliability, and significance of the solution. The …

This chapter discusses the problem of extrinsic self-calibration of camera networks. Self-
calibration in this context means that no artificial calibration object or any kind of user …

The quality of point correspondences is crucial for the successful application of multi camera
self-calibration procedures. There are several interest point detectors, local descriptors and …

We present a new method for solving the problem of camera pose and calibration from a
limited number of correspondences between noisy 2D and 3D features. We show that the …