title: Scale space analysis and active contours for omnidirectional images.
abstract: A new generation of optical devices that generate images covering a larger part of the field of view than conventional cameras, namely catadioptric cameras, is slowly emerging. These omnidirectional images will most probably deeply impact computer vision in the forthcoming years, provided that the necessary algorithmic background stands strong. In this paper, we propose a general framework that helps define various computer vision primitives. We show that geometry, which plays a central role in the formation of omnidirectional images, must be carefully taken into account while performing such simple tasks as smoothing or edge detection. Partial differential equations (PDEs) offer a very versatile tool that is well suited to cope with geometrical constraints. We derive new energy functionals and PDEs for segmenting images obtained from catadioptric cameras and show that they can be implemented robustly using classical finite difference schemes. Various experimental results illustrate the potential of these new methods on both synthetic and natural images.
authors.affiliation: Insitute of Microtechnology, Université de Neuchâtel, 2000 Neuchâtel, Switzerland. iva.bogdanova@unine.ch
authors.forename: Iva
authors.initials: I
authors.lastname: Bogdanova
authors.forename: Xavier
authors.initials: X
authors.lastname: Bresson
authors.forename: Jean-Philippe
authors.initials: JP
authors.lastname: Thiran
authors.forename: Pierre
authors.initials: P
authors.lastname: Vandergheynst
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D055096:Optical Devices; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity