Camera pose estimation plays a crucial role in stitching overlapped images captured by a camera to achieve a broad view of interest. In this paper, we propose a robust camera pose estimation approach to stitching images of a large 3D surface with known geometry. In particular, given a collection of images, we first construct a relative pose matrix estimation of all image pairs from the collection, where each entry of the matrix is calculated by solving a perspective-n-point (PnP) problem over the corresponding pair of images. To continue, we jointly estimate all camera poses by solving an optimization problem that exploits the underlying rank-2 relative pose matrices and the joint sparsity of camera pose errors. Finally, images are projected on to the 3D surface of interest based on estimated camera poses for the subsequent stitching process. Numerical experiments demonstrate that our proposed method outperforms existing methods in terms of both camera pose estimation and image stitching quality.

We present a novel algorithm to detect and remove cast shadows in a video sequence by taking advantage of the statistical prevalence of the shadowed regions over the object regions. We model shadows using multivariate Gaussians. We apply a weak classifier as a pre-filter. We project shadow models into a quantized color space to update a shadow flow function. We use shadow flow, background models, and current frame to determine the shadow and object regions. This method has several advantages: It does not require a color space transformation. We pose the problem in the RGB color space, and we can carry out the same analysis in other Cartesian spaces as well. It is data-driven and adapts to the changing shadow conditions. In other words, accuracy of our method is not limited by the preset values. Furthermore, it does not assume any 3D models for the target objects or tracking of the cast shadows between frames. Our results show that the detection performance is superior than the benchmark method.