Stabilization of infrared image sequence with differential invariants and M-estimators
Daniel McReynolds, Pascal Marchand, Yunlong Sheng, Langis Gagnon, Léandre Sévigny · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1999
We investigate computer vision techniques for the stabilization of image sequences from a single image sensor. Image stabilization is required to improve the performance of human operators for evaluating surveillance imagery in real-time. Non-trivial rotation and scale changes of the input images could be important. Furthermore, for many operations such as airborne surveillance, perspective distortion induces an image transformation that is typically not handled well by classical registration techniques such as cross-correlation. We focus on the issue of rotation, scale and projective invariance for point feature detection and verification. It is often the case that hypothesized point matches are incorrect or poorly localized so we investigate solutions incorporating robust estimators. Feature points are detected with the Harris-Stephens corner detector. We use the greylevel differential invariant (GDI) matching due to Schmid and Mohr which is invariant to rotation and scaling. Extensions to the basic GDI method are introduced that improve the performance of the method. We verify the point correspondences under orthographic projection using the epipolar constraint via M-estimators and least median of squares on real-world and synthesized IR sequences.