Adapting spectral scale for shape from texture

Abstract

Spectral analysis provides a powerful means of estimating the perspective pose of texture planes. Unfortunately, one of the problems that restricts the utility of the method is the need to set the size of the spectral window. For texture planes viewed under extreme perspective distortion, the spectral frequency density may vary rapidly across the image plane. If the size of the window is mismatched to the underlying texture distribution, then the estimated frequency spectrum may become severely defocussed. This in turn limits the accuracy of perspective pose estimation. The aim in this paper is to describe an adaptive method for setting the size of the spectral window. We provide an analysis which shows that there is a window size that minimises the degree of defocusing. The minimum is located through an analysis of the spectral covariance matrix. We experiment with the new method on both synthetic and real world imagery. This demonstrates that the method provides accurate pose angle estimates, even when the slant angle is large. We also provide a comparison of the accuracy of perspective pose estimation that results both from our adaptive scale method and with one of fixed scale.