Central Catadioptric Line Detection

Central catadioptric sensors enable to acquire panoramic images on a 360 degree field of view while preserving a single viewpoint. These advantages account for the growing use of these sensors in applications such as surveillance, navigation or modelling. However, the deformations of the image do not allow to apply classical perspective image algorithms or operators. Typically, straight line detection in perspective image becomes a delicate and complex conic detection problem in central catadioptric image. Previous methods proposed in the literature were essentially motivated by particular cases such as horizontal line detection or paracatadioptric line detection. In this paper, we propose an algorithm which consists in performing the detection in the space of the equivalent sphere which is the unified domain of central catadioptric sensors. On this sphere, real lines are projected into great circles that we detect thanks to the Hough transform. We also propose to apply this unifying model in order to perform the calibration of the intrinsic parameters required for the projection on the sphere. We show results on synthetic and real catadioptric images (parabolic, hyperbolic) to demonstrate the relevance of the detection on the sphere.