Distributed robust image mosaics

We describe a distributed robust image mosaics system that is robust to outlier and is better in computation efficiency. The system integrates a Web-based user interface, a robust image mosaic, and a distributed computing model. In robust image mosaics, we combine both feature-based and correlation-based approaches for image registration, and then incorporate a skipped mean estimator with Levenberg-Marquardt method for robust nonlinear parameter estimation. The distributed comparing model is used to speed up the computation of the global processing of image mosaics on a network of workstations. Load balancing strategies are proposed to improve the efficiency of parallel computation under uneven load situation. Experimental results show that our system is indeed robust and has significantly speedup in computation