Abstract:
This paper proposes a novel edge-based stitching method to detect moving objects and construct
mosaics from images. The method is a coarse-to-fine scheme which first estimates a
good initialization of camera parameters with two complementary methods and then refines
the solution through an optimization process. The two complementary methods are the edge
alignment and correspondence-based approaches, respectively. The edge alignment method
estimates desired image translations by checking the consistencies of edge positions between
images. This method has better capabilities to overcome larger displacements and lighting variations
between images. The correspondence-based approach estimates desired parameters from
a set of correspondences by using a new feature extraction scheme and a new correspondence
building method. The method can solve more general camera motions than the edge alignment
method. Since these two methods are complementary to each other, the desired initial estimate
can be obtained more robustly. After that, a Monte-Carlo style method is then proposed for
integrating these two methods together. In this approach, a grid partition scheme is proposed to
increase the accuracy of each try for finding the correct parameters. After that, an optimization
process is then applied to refine the above initial parameters. Different from other optimization
methods minimizing errors on the whole images, the proposed scheme minimizes errors only on
positions of features points. Since the found initialization is very close to the exact solution and
only errors on feature positions are considered, the optimization process can be achieved very
quickly. Experimental results are provided to verify the superiority of the proposed method.
