Most of previous image mosaicking techniques deal with stationary images that do not contain moving objects. But these moving objects cause serious errors on global motion estimation which is the core process of the image mosaicking since the global motion is estimated biased by local motions due to moving objects. There are some proposed techniques to effectively eliminate local motions and get precise global motion parameters but they have their own drawbacks, respectively.
In this paper a contour-based approach for mosaicking images that contain moving objects in them is presented. First, we extract contours from each image to be mosaicked. And then we estimate initial global motion. The key task of our work is how to eliminate local motions and obtain a precise global motion between two input images. To do this, we use three kinds of consistency check algorithm. Shape similarity consistency, scale consistency, and rigid transformation consistency. In these check processes, local movings are detected due to their motion vectors far different from the dominant one and removed in an iterative way. Besides, since we use contour information for image mosaicking, our approach is robust against the global gray level change between input images. Experimental results demonstrate the performance of our algorithm.