mlsanityData to 7 Oct 2026

A Survey of Orthogonal Moments for Image Representation: Theory, Implementation, and Evaluation

arXiv 2103.14799 ↗First posted 27 Mar 2021cs.CVcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 22 of its 255 references that are in the map. First posted on arXiv 27 Mar 2021.

Builds on

22 of 255 references.

References · 22

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10 more
  1. 2017Image Projective Invariantscited by 4
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 2; 2024: 2; 2025: 1; 2026: 1.

8 papers.

  1. 2022DeepWSD: Projecting Degradations in Perceptual Space to Wasserstein Distance in Deep Feature Spacecited by 19
  2. 2022A Principled Design of Image Representation: Towards Forensic Taskscited by 8
  3. 2023Representing Noisy Image Without Denoisingcited by 2
  4. 2024MomentsNeRF: Leveraging Orthogonal Moments for Few-Shot Neural Renderingcited by 2
  5. 2023Spatial-Frequency Discriminability for Revealing Adversarial Perturbationscited by 1
  6. 2024Hierarchical Invariance for Robust and Interpretable Vision Tasks at Larger Scalescited by 1
  7. 2025Polar Separable Transform for Efficient Orthogonal Rotation-Invariant Image Representationcited by 0
  8. 2026Varifold Moment Invariants for Sustainable and Explainable Contour Feature Extractioncited by 0