Rotation Recovery from Spherical Images without Correspondences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Correspondence-Free Determination of the Affine Fundamental Matrix
IEEE Transactions on Pattern Analysis and Machine Intelligence
Correspondence-free Structure from Motion
International Journal of Computer Vision
An Insect-Inspired Active Vision Approach for Orientation Estimation with Panoramic Images
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
Proceedings of the 30th DAGM symposium on Pattern Recognition
Vision-based robot homing in dynamic environments
RA '07 Proceedings of the 13th IASTED International Conference on Robotics and Applications
International Journal of Computer Vision
Proposition and comparison of catadioptric homography estimation methods
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
A probabilistic framework for correspondence and egomotion
WDV'05/WDV'06/ICCV'05/ECCV'06 Proceedings of the 2005/2006 international conference on Dynamical vision
Rotation estimation and vanishing point extraction by omnidirectional vision in urban environment
International Journal of Robotics Research
An accurate and robust visual-compass algorithm for robot-mounted omnidirectional cameras
Robotics and Autonomous Systems
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We present a novel approach for the estimation of 3Dmotion directly from two images using the Radon transform. We assume a similarity function defined on the cross-product of two images which assigns a weight to all feature pairs. This similarity function is integrated over all feature pairs that satisfy the epipolar constraint. This integration is equivalent to filtering the similarity function with a Dirac function embedding the epipolar constraint. The result of this convolution is a function of the five unknownmotion parameters with maxima at the positions of compatible rigid motions. The breakthrough is in the realization that the Radon transform is a filtering operator: If we assume that images are defined on spheres and the epipolar constraint is a group action of two rotations on two spheres, then the Radon transform is a convolution/correlation integral. We propose a new algorithm to compute this integral from the spherical harmonics of the similarity and Dirac functions. The resulting resolution in the motion space depends on the bandwidth we keep from the spherical transform. The strength of the algorithm is in avoiding a commitment to correspondences, thus being robust to erroneous feature detection, outliers, and multiple motions. The algorithm has been tested in sequences of real omnidirectional images and it outperforms correspondence-based structure from motion.