In this paper a novel framework of panoramic mosaicing is presented based on phase correlation

particle filter and intensity difference minimizing. Combining the characteristics of frequency domain and spatial domain we construct a panorama from un-calibrated images for global optimization. The alignment consists of two phases: local alignment and global alignment. In the process of local alignment the lower accuracy

the proposed method employs phase correlation and feature based particle filter sequentially

by which we can obtain the swiftness and robustness of phase correlation as well as the corrective function of feature based particle filter. In the process of global alignment

because the initial value generated by the local alignment is close to the optimum one

the iterated algorithm could converge quickly. Meanwhile

a huge parameter space might be introduced by global optimization. We develop a strategy to reduce the dimensions of parameter space. In the experiment

this system shows the efficiency and robustness in the case of varying illumination

without camera calibration

less overlapping and less knowledge of the scene.