A Survey of Orthogonal Moments for Image Representation: Theory, Implementation, and Evaluation
arXiv:2103.14799 · doi:10.1145/3479428
Abstract
Image representation is an important topic in computer vision and pattern recognition. It plays a fundamental role in a range of applications towards understanding visual contents. Moment-based image representation has been reported to be effective in satisfying the core conditions of semantic description due to its beneficial mathematical properties, especially geometric invariance and independence. This paper presents a comprehensive survey of the orthogonal moments for image representation, covering recent advances in fast/accurate calculation, robustness/invariance optimization, definition extension, and application. We also create a software package for a variety of widely-used orthogonal moments and evaluate such methods in a same base. The presented theory analysis, software implementation, and evaluation results can support the community, particularly in developing novel techniques and promoting real-world applications.
ACM Computing Surveys, Volume 55, Issue 1, January 2023, Article No 1, pp 1-35, https://doi.org/10.1145/3479428
References in corpus (6)
- The Theory of Quaternion Matrix Derivatives
- A comparison of dense region detectors for image search and fine-grained classification
- Through the eyes of a descriptor: Constructing complete, invertible descriptions of atomic environments
- Reflection Invariant and Symmetry Detection
- Isomorphism between Differential and Moment Invariants under Affine Transform
- Dual affine moment invariants