Perspective cameras are the most popular imaging sensors used in Computer Vision. However, many application fields including automotive, surveillance and robotics, require the use of wide angle cameras (e.g., fisheye) which allow to acquire a larger portion of the scene using a single device at the cost of the introduction of noticeable radial distortion in the images. Affine covariant feature detectors have proven successful in a variety of Computer Vision applications including object recognition, image registration and visual search. Moreover, their robustness to a series of variabilities related to both the scene and the image acquisition process has been thoroughly studied in the literature. In this paper, we investigate their effectiveness on fisheye images providing both theoretical and experimental analyses. As theoretical outcome, we show that even if the radial distortion is not an affine transformation, it can be locally approximated as a linear function with a reasonably small error. The experimental analysis builds on Mikolajczyk's benchmark to assess the robustness of three popular affine region detectors (i.e., Maximally Stable Extremal Regions (MSER), Harris and Hessian affine region detectors), with respect to different variabilities as well as radial distortion. To support the evaluations, we rely on the Oxford dataset and introduce a novel benchmark dataset comprising 50 images depicting different scene categories. The experiments show that the affine region detectors can be effectively employed directly on fisheye images and that the radial distortion is locally modelled as an additional affine variability.
we have built a benchmark dataset comprising 50 high resolution rectilinear images (5204 X 3472 pixels) to which we artificially add different percentages of radial distortion. The 50 images are a random selection of the 100 images included in our previously collected dataset presented in this paper. The original images have been acquired using a Canon 650D camera with a Canon EF-24mm lens and depict scenes taken by considering different categories according to the scene categorization proposed by Torralba and Oliva: indoor, outdoor, natural, handmade, urban, car, pedestrian, street.