Dynamic mosaicking: combining A* algorithm with fractional Brownian motion for an optimal seamline detection

Abstract

Image mosaicking is a combination of algorithms that use two or several images to create a single image. The resulting mosaic is a representation of a scene of the used images with a larger field of vision. However, since dynamic objects can exist in the overlap regions of these images, ghosting and parallax effects appear, therefore poor results are obtained. To overcome these unwanted effects and to achieve better results, a new method is presented in this paper. This approach uses a new way to detect dynamic objects in the common areas by using a fractional Brownian motion with a predetermined similarity function instead of a noise function, the Zero Normalized Cross Correlation. Thus, it will ensure that a map is created with each pixel having a unique value based on their surroundings even in homogeneous areas. Furthermore, this new approach combines the previously computed map with the machine learning algorithm A* for a fast and efficient way to find an optimal seamline. Consequently, the obtained experimental results were compared with different methods and better results were obtained as can be seen by a better quality seamline measure, a result mosaic without any artifacts and a faster computation time.