mouvement
Principal Components Analysis of Optical Snow
Many applications in computer vision use Principal Components Analysis (PCA), for example, in camera calibration, stereo, localization and motion estimation. We present a new and fast PCA-based method to analyze optical snow. Optical snow is a complex form of visual motion that occurs when an observer moves through a highly cluttered 3D scene. For this […] Lire la suite
MRF solutions for probabilistic optical flow formulations
In this paper we propose an efficient, non-iterative method for estimating optical flow. We develop a probabilistic framework that is appropriate for describing the inherent uncertainty in the brightness constraint due to errors in image derivative computation. We separate the flow into two one-dimensional representations and pose the problem of flow estimation as one of […] Lire la suite
Motion Without Structure
We propose a new paradigm, motion without structure, for determining the ego-motion between two frames. It is best suited for cases where reliable feature point correspondence is difficult, or for cases where the expected camera motion is large. The problem is posed as a five-dimensional search over the space of possible motions during which the […] Lire la suite
Direct Estimation of Rotation from Two Frames via Epipolar Search
A direct method for estimating the rotational motion between two image frames is developed. The algorithm does not require knowledge of image correspondences, optical flow or scene structure and only assumes approximate knowledge of the translational motion. Spatial and temporal intensity gradients are avoided, resulting in an algorithm that is noise resistant. Moreover, the algorithm […] Lire la suite