In this paper, we propose a model-based image registration method capable of detecting the true transformation model between two images. We incorporate a statistical model selection criterion to choose the true underlying transformation model. Therefore, the proposed algorithm is robust to degeneracy as any degeneracy is detected by the model selection component. In addition, the algorithm is robust to noise and outliers since any corresponding pair that does not undergo the chosen model is rejected by a robust fitting method adapted from the literature. Another important contribution of this paper is evaluating a number of different model selection criteria for image registration task. We evaluated all different entena based on different levels of noise. We conclude that CAIC and GBIC slightly outperform other criteria for this application. The next choices are GIC, SSD and MDL. Finally, we create panorama images using our registration algorithm. The panorama images show the success of this algorithm.