Image mosaicing is widely used in computer vision applications. Accurate and consistent alignment of sequence images
is the key issue to image mosaicing. In this paper, a globally consistent image mosaicing is proposed by taking account
of various uncertainties. The problem of global alignment of a sequence of images is considered as a stochastic
estimation problem. The transformation parameters of images are considered as system state. System augmentation
model and system observation model are constructed. The global homographies parameters of sequence images are
estimated recursively with augmented Kalman filter in a common state vector and covariance matrix. The proposed
image alignment method can handle the uncertainty efficiently and is globally consistent. Some experimental results are
provided to validate the performance of the proposed method.