A Survey On Rotation Invariance Of Orthogonal Moments And Transforms

The theory of moments and transforms is well established and widely applied to a number of computer vision, pattern recognition and image processing applications. A sub-class of these moments and transforms are used as rotation invariant global descriptors. There exist a number of techniques in literature whose authors claim superiority of their approaches. Consequently, many researchers face difficulties in deciding about the best or the near-best rotation invariant technique for their use. This paper reviews numerous state-of-the-art rotation invariant techniques based on moments and transforms by conducting a comparative performance analysis of various prominent approaches available in literature. To support the analysis of the survey, detailed experiments are conducted to compare performance of the various invariants using Zernike moments (ZMs) as a representative of all moments and transforms. The ZMs are selected because they are the most commonly used rotation invariant moments. Experiments are conducted on three standard databases which represent a wide variety of objects in different proportions of the shape and color cues. It is shown that the magnitude of moments of the gray-scale and multi-channel representation of color objects provides the overall best recognition results across a variety of classifiers. ? 2021 Elsevier B.V. All rights reserved.