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Helmet-fourier orthogonal moments for image representation and recognition

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Abstract

The orthogonal moments have recently achieved outstanding predictive performance and become an indispensable tool in a wide range of imaging and pattern recognition applications, including image reconstruction, image classification and object detection. We present in this paper, a new set of orthogonal functions, called “Orthogonal helmet functions.” Using these functions we introduce three new sets of orthogonal moments and their invariants to scaling, rotation and translation for image representation and recognition, named, respectively, “the orthogonal helmet-Fourier moments” for the gray-level images, the multi-channel orthogonal helmet-Fourier moments and the quaternion orthogonal helmet-Fourier moments (QHFMs) for the color images. We introduce a series of experimental tests in image analysis and pattern recognition to validate the theoretical framework of our approach. The performance of these feature vectors is compared with the existing orthogonal invariant moments. The results of the comparative study show the efficiency and the superiority of our three orthogonal invariant moments. Thanks to our orthogonal moments QHFMs, we were able to lift the image recognition quality with rate can reach \(2.06\%.\)

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Authors and Affiliations

  1. Laboratory of Computer Science, Signals, Automation and Cognitivism, Faculty of Sciences Dhar El, Mahrez, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Amal Hjouji

  2. LTI, Laboratory, EST, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Jaouad EL-Mekkaoui

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  1. Amal Hjouji
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  2. Jaouad EL-Mekkaoui
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Correspondence to Amal Hjouji.

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Hjouji, A., EL-Mekkaoui, J. Helmet-fourier orthogonal moments for image representation and recognition. J Supercomput 78, 13583–13623 (2022). https://doi.org/10.1007/s11227-022-04414-6

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