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

TL;DR

This survey comprehensively reviews orthogonal moments for image representation, highlighting recent advances in computation, robustness, and applications, supported by software tools and evaluation to aid future research and practical use.

Contribution

It provides an extensive overview of recent developments in orthogonal moments, including theory, implementation, and evaluation, along with a software package for widespread use.

Findings

Enhanced calculation speed and accuracy of orthogonal moments

Improved robustness and invariance properties demonstrated

Evaluation results guide future research and application development

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.