Correspondenceless Stereo and Motion: Planar Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Regular Article: Computing Fourier Transforms and Convolutions on the 2-Sphere
Advances in Applied Mathematics
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Catadioptric Projective Geometry
International Journal of Computer Vision
Scalable Extrinsic Calibration of Omni-Directional Image Networks
International Journal of Computer Vision
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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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.