On the Two-View Geometry of Unsynchronized Cameras

We present new methods for simultaneously estimating camera geometry and time shift from video sequences from multiple unsynchronized cameras. Algorithms for simultaneous computation of a fundamental matrix or a homography with unknown time shift between images are developed. Our methods use minimal correspondence sets (eight for fundamental matrix and four and a half for homography) and therefore are suitable for robust estimation using RANSAC. Furthermore, we present an iterative algorithm that extends the applicability on sequences which are significantly unsynchronized, finding the correct time shift up to several seconds. We evaluated the methods on synthetic and wide range of real world datasets and the results show a broad applicability to the problem of camera synchronization.Comment: 12 pages, 9 figures, Computer Vision and Pattern Recognition (CVPR) 201

PanCam Bundle Adjustment

This thesis investigated the method of Bundle Adjustment with special focus on constrained systems, such as panoramic cameras. SBA algorithm from Lourakis and Argyros was implemented in Matlab and its functionality was validated. A new algorithm was proposed and implemented based the original one, incorporating the constraints suited for PanCam-like cameras. This algorithm was evaluated on both synthetic and real datasets. The performance was thoroughly analyzed and compared to the original method in terms of accuracy of the reconstruction. For synthetic datasets, significant improvements have been observed and expressed quantitatively. Particular improvements in terms of visual appearance have been observed on real datasets. Results were elaborated and possibilities for further improvement have been proposed.Validerat; 20110926 (anonymous

PanCam Bundle Adjustment

This thesis investigated the method of Bundle Adjustment with special focus on constrained systems, such as panoramic cameras. SBA algorithm from Lourakis and Argyros was implemented in Matlab and its functionality was validated. A new algorithm was proposed and implemented based the original one, incorporating the constraints suited for PanCam-like cameras. This algorithm was evaluated on both synthetic and real datasets. The performance was thoroughly analyzed and compared to the original method in terms of accuracy of the reconstruction. For synthetic datasets, significant improvements have been observed and expressed quantitatively. Particular improvements in terms of visual appearance have been observed on real datasets. Results were elaborated and possibilities for further improvement have been proposed.Validerat; 20110926 (anonymous

Rolling Shutter Camera Absolute Pose

We present minimal, non-iterative solutions to the absolute pose problem for images from rolling shutter cameras. The absolute pose problem is a key problem in computer vision and rolling shutter is present in a vast majority of today's digital cameras. We discuss several camera motion models and propose two feasible rolling shutter camera models for a polynomial solver. In previous work a linearized camera model was used that required an initial estimate of the camera orientation. We show how to simplify the system of equations and make this solver faster. Furthermore, we present a first solution of the non-linearized camera orientation model using the Cayley parameterization. The new solver does not require any initial camera orientation estimate and therefore serves as a standalone solution to the rolling shutter camera pose problem from six 2D-to-3D correspondences. We show that our algorithms outperform P3P followed by a non-linear refinement using a rolling shutter model