Multi-View Geometry for Camera Networks

Abstract

Designing computer vision algorithms for camera networks requires an understanding of how images of the same scene from different viewpoints are related. This chapter introduces the basics of multiview geometry in computer vision, including image formation and camera matrices, epipolar geometry and the fundamental matrix, projective transformations, and N -camera geometry. We also discuss feature detection and matching, and describe basic estimation algorithms for the most common problems that arise in multiview geometry.

3D particle tracking velocimetry for the determination of temporally resolved particle trajectories within laser powder bed fusion of metals

“…fund It allows to validate a matching set of projections, consisting of a point within the left (x, y) and the right image (x′, y′) in form of the condition [21]:…”

“…The set of points within the image plane P' of the right camera matching to a point u in the right image plane P, forms the epipolar line l u [21] and vice versa.…”

“…In accordance with the description used for the particle detection, the particles resemble each other; therefore, no features can be used to differentiate them, as stated in [21]. Due to this, it is not possible to identify false pairs at this step within the algorithm.…”