Image invariants to anisotropic Gaussian blur

Kostková, Jitka; Flusser, Jan; Lébl, Matěj; Pedone, Matteo (2019-05-12)
 
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https://doi.org/10.1007/978-3-030-20205-7_12

Kostková, Jitka
Flusser, Jan
Lébl, Matěj
Pedone, Matteo
Springer Nature
12.05.2019

Kostková J., Flusser J., Lébl M., Pedone M. (2019) Image Invariants to Anisotropic Gaussian Blur. In: Felsberg M., Forssén PE., Sintorn IM., Unger J. (eds) Image Analysis. SCIA 2019. Lecture Notes in Computer Science, vol 11482. Springer, Cham

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© Springer Nature Switzerland AG 2019. This is a post-peer-review, pre-copyedit version of an article published in Scandinavian Conference on Image Analysis. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-20205-7_12.
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doi:https://doi.org/10.1007/978-3-030-20205-7_12
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https://urn.fi/URN:NBN:fi-fe202001101746
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Abstract

The paper presents a new theory of invariants to Gaussian blur. Unlike earlier methods, the blur kernel may be arbitrary oriented, scaled and elongated. Such blurring is a semi-group action in the image space, where the orbits are classes of blur-equivalent images. We propose a non-linear projection operator which extracts blur-insensitive component of the image. The invariants are then formally defined as moments of this component but can be computed directly from the blurred image without an explicit construction of the projections. Image description by the new invariants does not require any prior knowledge of the particular blur kernel shape and does not include any deconvolution. Potential applications are in blur-invariant image recognition and in robust template matching.

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