Summary

This thesis has developed a coherent framework for analysing image sequences based on the affine camera, and has demonstrated the practical feasibility of recovering 3D structure and motion in a bottom–up fashion, using “corner” features. New algorithms have been proposed to compute affine structure, and these have then been applied to the problems of clustering and view transfer. The theory of affine epipolar geometry has been derived and applied to outlier rejection and rigid motion estimation. Due consideration has been paid to error and noise models, with a χ2 test serving as a termination criterion for cluster growth and outlier detection, and confidence limits in the motion parameters facilitating Kalman filtering.

On a practical level, all the algorithms have been implemented and tested on a wide range of sequences. The use of n points and m frames has lead to enhanced noise immunity and has also simplified the algorithms in important ways, e.g. local coordinate frames are no longer needed to compute affine structure or rigid motion parameters. Finally, the use of 3D information without explicit depth has been illustrated in a working system (e.g. for transfer).

In summary, the affine camera has been shown to provide a solid foundation both for understanding structure and motion under parallel projection, and for devising reliable algorithms.

Future work

There are many interesting problems for future work to address. First, the CI space interpretation of the motion segmentation problem is that each independently moving object contributes a different 3D linear subspace.