Scale-Space for Discrete Signals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Panoramic representation for route recognition by a mobile robot
International Journal of Computer Vision - Special issue on machine vision research at Osaka University
A maximum likelihood stereo algorithm
Computer Vision and Image Understanding
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
International Journal of Computer Vision
Depth Discontinuities by Pixel-to-Pixel Stereo
International Journal of Computer Vision
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
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
Fast panoramic stereo matching using cylindrical maximum surfaces
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Processing Sparse Panoramic Images via Space Variant Operators
Journal of Mathematical Imaging and Vision
Local visual homing by warping of two-dimensional images
Robotics and Autonomous Systems
Three 2D-warping schemes for visual robot navigation
Autonomous Robots
3D camera pose estimation using line correspondences and 1D homographies
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
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We define a family of novel interest operators for extracting features from one-dimensional panoramic images for use in mobile robot navigation. Feature detection proceeds by applying local interest operators in the scale space of a 1D circular image formed by averaging the center scanlines of a cylindrical panorama. We demonstrate that many such features remain stable over changes in viewpoint and in the presence of noise and camera vibration, and define a feature descriptor that collects shape properties of the scale-space surface and color information from the original images. We then present a novel dynamic programming method to establish globally optimal correspondences between features in images taken from different viewpoints. Our method can handle arbitrary rotations and large numbers of missing features. It is also robust to significant changes in lighting conditions and viewing angle, and in the presence of some occlusion.