Novel Quaternion Orthogonal Mountain Fourier Moments for Pattern Recognition Applications

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.

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