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Bibliografie

Conference Paper (international conference)

Orthogonal Affine Invariants from Gaussian-Hermite Moments

Flusser Jan, Suk Tomáš, Yang B.

: Computer Analysis of Images and Patterns : CAIP 2019 International Workshops, ViMaBi and DL-UAV, Salerno, Italy, September 6, 2019, Proceedings, p. 413-424 , Eds: Vento M., Percannella G., Colantonio S., Giorgi D., Matuszewski B. J., Kerdegari H., Razaak M.

: International Conference on Computer Analysis of Images and Patterns, CAIP 2019 /18./, (Salerno, IT, 20190902)

: GA18-07247S, GA ČR

: Affine transformation, Invariants, Image normalization, Gaussian-Hermite moments

: 10.1007/978-3-030-29891-3_36

: http://library.utia.cas.cz/separaty/2019/ZOI/flusser-0508021.pdf

(eng): We propose a new kind of moment invariants with respect to an affine transformation. The new invariants are constructed in two steps. First, the affine transformation is decomposed into scaling, stretching and two rotations. The image is partially normalized up to the second rotation, and then rotation invariants from Gaussian-Hermite moments are applied. Comparing to the existing approaches – traditional direct affine invariants and complete image normalization – the proposed method is more numerically stable. The stability is achieved thanks to the use of orthogonal Gaussian-Hermite moments and also due to the partial normalization, which is more robust to small changes of the object than the complete normalization. Both effects are documented in the paper by experiments. Better stability opens the possibility of calculating affine invariants of higher orders with better discrimination power. This might be useful namely when different classes contain similar objects and cannot be separated by low-order invariants.

: JD

: 20206

07.01.2019 - 08:39