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Image mosaicking is the process of piecing together multiple video frames or still images from a moving camera
to form a wide-area or panoramic view of the scene being imaged. Mosaics have widespread applications in many
areas such as security surveillance, remote sensing, geographical exploration, agricultural field surveillance, virtual
reality, digital video, and medical image analysis, among others. When mosaicking a large number of still images
or video frames, the quality of the resulting mosaic is compromised by projective distortion. That is, during the
mosaicking process, the image frames that are transformed and pasted to the mosaic become significantly scaled
down and appear out of proportion with respect to the mosaic. As more frames continue to be transformed,
important target information in the frames can be lost since the transformed frames become too small, which
eventually leads to the inability to continue further. Some projective distortion correction techniques make
use of prior information such as GPS information embedded within the image, or camera internal and external
parameters. Alternatively, this paper proposes a new algorithm to reduce the projective distortion without
using any prior information whatsoever. Based on the analysis of the projective distortion, we approximate the
projective matrix that describes the transformation between image frames using an affine model. Using singular
value decomposition, we can deduce the affine model scaling factor that is usually very close to 1. By resetting the
image scale of the affine model to 1, the transformed image size remains unchanged. Even though the proposed
correction introduces some error in the image matching, this error is typically acceptable and more importantly,
the final mosaic preserves the original image size after transformation. We demonstrate the effectiveness of this
new correction algorithm on two real-world unmanned air vehicle (UAV) sequences. The proposed method is
shown to be effective and suitable for real-time implementation.
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