If reliable and general computer vision techniques are to be developed it is crucial that we find ways of characterizing low-level image features with invariant quantities. For example, if edge significance could be measured in a way that was invariant to image illumination and contrast, higher-level image processing operations could be conducted with much greater confidence. However, despite their importance, little attention has been paid to the need for invariant quantities in low-level vision for tasks such as feature detection or feature matching. This thesis develops a number of invariant low-level image measures for feature detection, local symmetry/asymmetry detection, and for signal matching. These invariant quantities are developed from representations of the image in the frequency domain. In particular, phase data is used as the fundamental building block for constructing these measures. Phase congruency is developed as an illumination and contrast invariant measure of feature significance. This allows edges, lines and other features to be detected reliably, and fixed thresholds can be applied over wide classes of images. Points of local symmetry and asymmetry in images give rise to special arrangements of phase, and these too can be characterized by invariant measures. Finally, a new approach to signal matching that uses correlation of local phase and amplitude information is developed. This approach allows reliable phase based disparity measurements to be made, overcoming many of the difficulties associated with scale-space singularities.
|Doctor of Philosophy
|Unpublished - 1996