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Generic spatial-color metric for scale-space processing of catadioptric images.

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Title: Generic spatial-color metric for scale-space processing of catadioptric images.
Authors: Aziz, Fatima1,2 fatima.aziz@unilim.fr, Labbani-Igbida, Ouiddad1, Radgui, Amina2, Tamtaoui, Ahmed2
Source: Computer Vision & Image Understanding. Nov2018, Vol. 176, p54-69. 16p.
Subjects: Catadioptric systems, Omnirange system, Gaussian function, Color image processing, Image processing
Abstract: Abstract Images produced by omnidirectional catadioptric systems provide a larger field of view than conventional cameras. However, these images contain significant radial distortions making classical processing unadapted. In addition, color information is almost neglected in omnidirectional imaging. In this paper, we propose a unifying framework, for central catadioptric color image processing, using Riemannian embedding that deals simultaneously with the geometric deformation due to the use of curved mirrors, and the multi-dimensional characteristic of the image. Based on the introduced Riemannian metric, we derive an adapted Gaussian kernel which is essential in widely used image processing. The resulting new formulation is then applied to various image processing: Image smoothing, Difference of Gaussians filtering and scale-space analysis, edge extraction and corner feature detection using Gaussian derivatives. The experiments illustrate the potential of the proposed approach, and show the higher quality of the adapted processing. Highlights • A new generic metric to process images directly in the catadioptric plane. • The proposed metric encapsulates color information and mirror distorsion. • An adapted Riemannian Gaussian kernel is derived accordingly. • A scale space analysis is performed to compare different metrics. • Effectiveness is proved in smoothing, edge and corner detection. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Abstract Images produced by omnidirectional catadioptric systems provide a larger field of view than conventional cameras. However, these images contain significant radial distortions making classical processing unadapted. In addition, color information is almost neglected in omnidirectional imaging. In this paper, we propose a unifying framework, for central catadioptric color image processing, using Riemannian embedding that deals simultaneously with the geometric deformation due to the use of curved mirrors, and the multi-dimensional characteristic of the image. Based on the introduced Riemannian metric, we derive an adapted Gaussian kernel which is essential in widely used image processing. The resulting new formulation is then applied to various image processing: Image smoothing, Difference of Gaussians filtering and scale-space analysis, edge extraction and corner feature detection using Gaussian derivatives. The experiments illustrate the potential of the proposed approach, and show the higher quality of the adapted processing. Highlights • A new generic metric to process images directly in the catadioptric plane. • The proposed metric encapsulates color information and mirror distorsion. • An adapted Riemannian Gaussian kernel is derived accordingly. • A scale space analysis is performed to compare different metrics. • Effectiveness is proved in smoothing, edge and corner detection. [ABSTRACT FROM AUTHOR]
ISSN:10773142
DOI:10.1016/j.cviu.2018.09.002