Department of Computer Science
The University of Western Australia
AbstractImage features such as step edges, lines, and Mach bands all give rise to points where the Fourier components of the image are maximally in phase. The use of phase congruency for marking features has significant advantages over gradient-based methods. Phase congruency is a dimensionless quantity that is invariant to changes in image brightness or contrast; hence, it provides an absolute measure of the significance of feature points, thus allowing the use of universal threshold values that can be applied over wide classes of images. This paper presents a new measure of phase congruency and shows how it can be calculated through the use of wavelets. The existing theory that has been developed for 1-D signals is extended to allow the calculation of phase congruency in 2-D images. It is shown that, for good localization, it is important to consider the spread of frequencies present at a point of phase congruency. An effective method for identifying and compensating for the level of noise in an image is presented. Finally, it is argued that high-pass filtering should be used to obtain image information at different scales. With this approach, the choice of scale affects only the relative significance of features without degrading their localization.
Keywords: phase congruency, feature detection, low-level invariance, log Gabor wavelets, noise compensation