Novel Quaternion Orthogonal Mountain Fourier Moments for Pattern Recognition Applications

doi:10.1007/s42967-024-00412-y

Abstract

Abstract: Recent advances have been made in a wide range of imaging and pattern recognition applications, including picture categorization and object identification systems. These systems necessitate a robust feature extraction method. This study proposes a new class of orthogonal functions known as orthogonal mountain functions (OMFs). Using these functions, a novel set of orthogonal moments and associated scaling, rotation, and translation (SRT) invariants are presented for building a color image’s feature vector components. These orthogonal moments are presented as quaternion orthogonal mountain Fourier moments (QOMFMs). To demonstrate the validity of our theoretically recommended technique, we conduct a number of image analysis and pattern recognition experiments, including a comparison of the performance of the feature vectors proposed above to preexisting orthogonal invariant moments. The result of this study experimentally proves the effectiveness and quality of our QOMFMs.

Key words: Orthogonal mountain functions (OMFs), Mountain Fourier invariant moments, Pattern recognition, Quaternion invariant mountain Fourier moments

CLC Number:

Boujamaa Janati Idrissi, Yahya Sahmoudi, Omar El Ogri, Jaouad El-Mekkaoui. Novel Quaternion Orthogonal Mountain Fourier Moments for Pattern Recognition Applications[J]. Communications on Applied Mathematics and Computation, 2026, 8(1): 110-129.

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