[2408.07687] RSD-DOG : A New Image Descriptor based on Second Order Derivatives
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Computer Science > Computer Vision and Pattern Recognition

arXiv:2408.07687 (cs)
[Submitted on 14 Aug 2024]

Title:RSD-DOG : A New Image Descriptor based on Second Order Derivatives

Authors:Darshan Venkatrayappa, Philippe Montesinos, Daniel Diep, Baptiste Magnier
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Abstract:This paper introduces the new and powerful image patch descriptor based on second order image statistics/derivatives. Here, the image patch is treated as a 3D surface with intensity being the 3rd dimension. The considered 3D surface has a rich set of second order features/statistics such as ridges, valleys, cliffs and so on, that can be easily captured by using the difference of rotating semi Gaussian filters. The originality of this method is based on successfully combining the response of the directional filters with that of the Difference of Gaussian (DOG) approach. The obtained descriptor shows a good discriminative power when dealing with the variations in illumination, scale, rotation, blur, viewpoint and compression. The experiments on image matching, demonstrates the advantage of the obtained descriptor when compared to its first order counterparts such as SIFT, DAISY, GLOH, GIST and LIDRIC.
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2408.07687 [cs.CV]
  (or arXiv:2408.07687v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2408.07687

Submission history

From: Darshan Venkatrayappa [view email]
[v1] Wed, 14 Aug 2024 17:41:45 UTC (2,913 KB)
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