A new linear algorithm for calibrating central catadioptric cameras
Pattern Recognition
Fast Central Catadioptric Line Extraction
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Motion estimation by decoupling rotation and translation in catadioptric vision
Computer Vision and Image Understanding
Hypercatadioptric line images for 3D orientation and image rectification
Robotics and Autonomous Systems
A Fisher-Rao Metric for Paracatadioptric Images of Lines
International Journal of Computer Vision
A unified framework for line extraction in dioptric and catadioptric cameras
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
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A line in space is projected to a conic in the central catadioptric image, and such a conic is called a line image. This paper proposes a novel approach for efficiently detecting line images using Hough transform. Detecting line images brings two novel challenges for conic detection: one is that effects of occlusion are very significant where traditional conic detecting methods may fail, the other is that line images can belong to any type of conic, such as, line, circle, ellipse, hyperbola, parabola etc., and it is very difficult to detect a conic when its type is unknown. The main contribution of this work is that we prove a line image can be parameterized by only two parameters on the Gaussian sphere rather than five ones as a generic conic requires, and the above two challenges can be substantially solved accordingly. The validity of our proposed approach is illustrated by experiments.