New set of non-separable 2D and 3D invariant moments for image representation and recognition

Extraction of the Microstructure of Wool Fabrics Based on Structure Tensor

“…The parameters of the elliptical template generated by the cross-section are calculated by the geometric moment and central moment [ 45 ]. Considering that the shape of the cross-section is the main object of interest, the image is binarized to eliminate the effect of grayscale.…”

“…The orientation of zero-valued pixels is set to 90°, which means that yarns parallel to the slice plane are transformed into the background. The parameters of the elliptical template generated by the cross-section are calculated by the geometric moment and central moment [45]. Considering that the shape of the cross-section is the main object of interest, the image is binarized to eliminate the effect of grayscale.…”

Improving the Machine Learning Performance for Image Recognition Using a New Set of Mountain Fourier Moments

“…For this characteristic to be powerful, it must be at least invariant in rotation, in translation and in scale. And for this reason, in recent years, applications on image analysis and pattern recognition have known very important developments including; image identification by Hu (1962), collected and image recovery by Teague (1980), infrared analysis by Zhang et al (2009), English and Chinese Letters analysis by Hjouji et al (2021a), walking detection by Lahouli et al (2018), dot spots by Hjouji et al (2021b), image noise by Ji et al (2009), face identification by El-Mekkaoui et al (2021), image description by Hosny and Darwish (2018), color form test by Assefa et al (2010), 3Dim image identification by Batioua et al (2017), image content by Singh (2012), image evaluation by El Ogri et al (2020), robust detection by , pattern storage by Hmimid et al (2015), use of sketches by Ansary et al (2006), scene report by Lin et al (2008), eye diseases detection and classification by Jenny et al (2023), correction of noisy images by Chen et al (2022), an accurate segmentation of the object of interest by Vite-Chávez et al (2023) …etc. In this article we base ourselves on the principles of orthogonal moments, Hu (1962) first proposed an extraction feature using non-orthogonal invariant moments.…”