Explicit quaternion krawtchouk moment invariants for finger-spelling sign language recognition

Elouariachi, Ilham; Benouini, Rachid; Zenkouar, Khalid; Zarghili, Arsalane; Fadili, Hakim El

doi:10.23919/eusipco47968.2020.9287845

Cited by 9 publications

(2 citation statements)

References 14 publications

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“…To evaluate the proposed algorithm, a comprehensive test has been conducted. The results of the proposed method with six energy-based algorithms, such as RBF kernel, 24 Krawtchouk, 25 TextureRBF, 26 L2S, 22 ABC-level set, 27 and customized RBF 28 and using four datasets Brodatz, 29 FABRIC 30 artificial images and real images has been compared. It should be noted that these datasets have a total of 120 images.…”

Section: Resultsmentioning

confidence: 99%

Local energy-based image region segmentation using Legendre polynomials

Mahmoudian

Niazi

2023

J. Electron. Imag.

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.Segmentation of images is a critical pre-processing step in image processing. The objective of segmentation is to divide an image into two homogeneous segments based on similar features. Recently, energy-based segmentation methods, such as the level set algorithm, have exhibited remarkable performance in segmenting various types of image data. However, such methods fail to produce satisfactory results on textured images, leading to inaccurate foreground and background segmentation. To address this issue, the proposed method utilizes a feature space based on image local energy, which is independent of image intensity. This feature space provides a more robust representation of texture in the image. By taking advantage of a unique feature space, images can be represented with a sum of Legendre polynomials. The reproduced image is injected into the L2S algorithm. The level set function is optimized from the average difference of Legendre polynomial coefficients to provide the best representation of the image. Consequently, the proposed method results in better segmentation accuracy, with an average increase of 19.33 compared to existing state-of-the-art methods.

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Section: Resultsmentioning

confidence: 99%

Local energy-based image region segmentation using Legendre polynomials

Mahmoudian

Niazi

2023

J. Electron. Imag.

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“…The quaternion method represents an image as a three-dimensional vector describing the components of the color image, which effectively uses the color information of different channels of the color image. Elouariachi et al [11] derived a new set of quaternion Krawtchouk moments (QKMs) and explicit quaternion Krawtchouk moment invariants (EQKMIs), which can be applied to fingerspelling sign language recognition. Wang et al [12,13] constructed a class of quaternion color orthogonal moments based on quaternion theory.…”

Section: Introductionmentioning

confidence: 99%

Quaternion fractional-order color orthogonal moment-based image representation and recognition

Yang

et al. 2021

J Image Video Proc.

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Inspired by quaternion algebra and the idea of fractional-order transformation, we propose a new set of quaternion fractional-order generalized Laguerre orthogonal moments (QFr-GLMs) based on fractional-order generalized Laguerre polynomials. Firstly, the proposed QFr-GLMs are directly constructed in Cartesian coordinate space, avoiding the need for conversion between Cartesian and polar coordinates; therefore, they are better image descriptors than circularly orthogonal moments constructed in polar coordinates. Moreover, unlike the latest Zernike moments based on quaternion and fractional-order transformations, which extract only the global features from color images, our proposed QFr-GLMs can extract both the global and local color features. This paper also derives a new set of invariant color-image descriptors by QFr-GLMs, enabling geometric-invariant pattern recognition in color images. Finally, the performances of our proposed QFr-GLMs and moment invariants were evaluated in simulation experiments of correlated color images. Both theoretical analysis and experimental results demonstrate the value of the proposed QFr-GLMs and their geometric invariants in the representation and recognition of color images.

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