Blurred image recognition by Legendre moment invariants.

Hui Zhang; Huazhong Shu; Guo-Niu Han; Gouenou Coatrieux; Limin Luo; Jean-Louis Coatrieux

inserm-00437044, version 1

Blurred image recognition by Legendre moment invariants.

Hui Zhang ^1, 2, Huazhong Shu ( Auteur à contacter de préférence ) ^1, 2, Guo-Niu Han ³, Gouenou Coatrieux ^4, 5, Limin Luo () ^1, 2, Jean-Louis Coatrieux () ^2, 6

IEEE Transactions on Image Processing 19, 3 (2010) 596-611

Résumé : Processing blurred images is a key problem in many image applications. Existing methods to obtain blur invariants which are invariant with respect to centrally symmetric blur are based on geometric moments or complex moments. In this paper, we propose a new method to construct a set of blur invariants using the orthogonal Legendre moments. Some important properties of Legendre moments for the blurred image are presented and proved. The performance of the proposed descriptors is evaluated with various point-spread functions and different image noises. The comparison of the present approach with previous methods in terms of pattern recognition accuracy is also provided. The experimental results show that the proposed descriptors are more robust to noise and have better discriminative power than the methods based on geometric or complex moments.

1 : Laboratory of Image Science and Technology (LIST)
SouthEast University – School of Computer Science and Engineering
2 : Centre de Recherche en Information Biomédicale sino-français (CRIBS)
INSERM : LABORATOIRE INTERNATIONAL ASSOCIÉ – Université de Rennes 1 – SouthEast University
3 : Institut de Recherche Mathématique Avancée (IRMA)
CNRS : UMR7501 – Université Louis Pasteur - Strasbourg I
4 : Département Image et Traitement Information (ITI)
Institut Mines-Télécom – Télécom Bretagne – PRES Université Européenne de Bretagne [UEB]
5 : Laboratoire de Traitement de l'Information Medicale (LATIM)
INSERM : U650 – Université de Bretagne Occidentale [UBO] – Institut Mines-Télécom – Télécom Bretagne – CHU Brest – PRES Université Européenne de Bretagne [UEB]
6 : Laboratoire Traitement du Signal et de l'Image (LTSI)
INSERM : U642 – Université de Rennes 1

inserm-00437044, version 1
http://www.hal.inserm.fr/inserm-00437044
oai:www.hal.inserm.fr:inserm-00437044
Contributeur : Lotfi Senhadji <>
Soumis le : Lundi 30 Novembre 2009, 15:53:31
Dernière modification le : Jeudi 1 Décembre 2011, 15:24:43

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PubMed : 19933003

PubMed Central : 3245248

DOI : 10.1109/TIP.2009.2036702

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%% inserm-00437044, version 1
%% http://www.hal.inserm.fr/inserm-00437044
@article{zhang:inserm-00437044,
    hal_id = {inserm-00437044},
    url = {http://www.hal.inserm.fr/inserm-00437044},
    title = {{Blurred image recognition by Legendre moment invariants.}},
    author = {Zhang, Hui and Shu, Huazhong and Han, Guo-Niu and Coatrieux, Gouenou and Luo, Limin and Coatrieux, Jean-Louis},
    abstract = {{Processing blurred images is a key problem in many image applications. Existing methods to obtain blur invariants which are invariant with respect to centrally symmetric blur are based on geometric moments or complex moments. In this paper, we propose a new method to construct a set of blur invariants using the orthogonal Legendre moments. Some important properties of Legendre moments for the blurred image are presented and proved. The performance of the proposed descriptors is evaluated with various point-spread functions and different image noises. The comparison of the present approach with previous methods in terms of pattern recognition accuracy is also provided. The experimental results show that the proposed descriptors are more robust to noise and have better discriminative power than the methods based on geometric or complex moments.}},
    keywords = {Blur Invariants; Blurred Image; Data mining; Gaussian noise; Imaging; Legendre Moments; Pattern Recognition; Pixel; Robustness; Symmetric Blur.},
    language = {Anglais},
    affiliation = {Laboratory of Image Science and Technology - LIST , Centre de Recherche en Information Biom{\'e}dicale sino-fran{\c c}ais - CRIBS , Institut de Recherche Math{\'e}matique Avanc{\'e}e - IRMA , D{\'e}partement Image et Traitement Information - ITI , Laboratoire de Traitement de l'Information Medicale - LATIM , Laboratoire Traitement du Signal et de l'Image - LTSI},
    pages = {596-611},
    journal = {IEEE Transactions on Image Processing},
    volume = {19},
    number = {3 },
    audience = {internationale },
    issn = {1941-0042 },
    doi = {10.1109/TIP.2009.2036702 },
    year = {2010},
    month = Mar,
    pdf = {http://www.hal.inserm.fr/inserm-00437044/PDF/TIP.pdf, http://www.hal.inserm.fr/inserm-00437044/PDF/inserm-00437044\_edited.pdf},
}

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