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Recently, more and more research has been done on micro air vehicles (MAVs). An autonomous flight control system is necessary for developing practical MAVs to be used for a wide array of missions. Due to the limitations of size, weight, and power, MAVs have the very low payload capacity and moments of inertia. The current technologies with rate and acceleration sensors applied on larger aircrafts are impractical to MAVs, and they are difficult to be scaled down to satisfy the demands of MAVs. Since surveillance has been considered as the primary mission of MAVs, it is essential for MAVs to be equipped with on-board imaging sensors such as cameras, which have rich information content. So vision-based techniques, without increasing the MAVs payload, may be a feasible idea for flight autonomy of MAVs. In this paper, a new robust horizon extraction algorithm based on the orientation projection method is proposed, which is the foundation of a vision-based flight control system. The horizon extraction algorithm is effective for both color images and gray images. The horizon can be extracted not only from fine images captured in fair conditions but also from blurred images captured in cloudy, even foggy days. In order to raise the computational speed to meet real-time requirements, the algorithmic optimization is also discussed in the paper, which is timesaving by narrowing the seeking scope of orientations and adopting the table look-up method. According to the orientation and position of the horizon in the image, two important angular attitude parameters for stability and control, the roll angle and the pitch angle, could be calculated. Several experimental results demonstrate the feasibility and robustness of the algorithm.