General structure-from-motion methods are not adept at dealing with constrained camera motions, even though such motions greatly simplify vision tasks like mobile robot localization. Typical ego-motion techniques designed for such a purpose require locating feature correspondences between images. However, there are many cases where features cannot be matched robustly. For example, images from panoramic sensors are limited by nonuniform angular sampling, which can complicate the feature matching process under wide baseline motions. In this paper we compute the planar ego-motion of a spherical sensor without correspondences. We propose a generalized Hough transform on the space of planar motions. Our transform directly processes the information contained within all the possible feature pair combinations between two images, thereby circumventing the need to isolate the best corresponding matches. We generate the Hough space in an efficient manner by studying the spectral information contained in images of the feature pairs, and by re-treating our Hough transform as a correlation of such feature pair images.