Two View Line-Based Matching, Motion Estimation and Reconstruction for Central Imaging Systems

The primary goal of this thesis is to develop generic motion and structure algorithms for images taken from constructed scenes by various types of central imaging systems including perspective, fish-eye and catadioptric systems. Assuming that the mapping between the image pixels and their 3D rays in space is known, instead of image planes, we work on image spheres (projection of the images on a unit sphere) which enable us to present points over the entire view sphere suitable for presenting omnidirectional images. In the first part of this thesis, we develop a generic and simple line matching approach for images taken from constructed scenes under a short baseline motion as well as a fast and original geometric constraint for matching lines in planar constructed scenes insensible to the motion of the camera for all types of central images including omnidirectional images. Next, we introduce a unique and efficient way of computing overlap between two segments on perspective images which considerably decreases the overall computational time of a segment-based motion estimation and reconstruction algorithm. Finally in last part of this thesis, we develop a simple motion estimation and surface reconstruction algorithm for piecewise planar scenes applicable to all kinds of central images which uses only two images and is based on minimum line correspondences. To demonstrate the performance of these algorithms we experiment with various real images taken by a simple perspective camera, a fish-eye lens, and two different kinds of paracatadioptric sensors, the first one is a folded catadioptric camera and the second one is a classic paracatadioptric system composed of a parabolic mirror in front of a telecentric lens.