A Survey of Orthogonal Moments for Image Representation: Theory, Implementation, and Evaluation∗
A Survey of Orthogonal Moments for Image Representation: Theory, Implementation, and Evaluation∗ SHUREN QI, YUSHU ZHANG, and CHAO WANG, Nanjing University of Aeronautics and Astro- nautics, China JIANTAO ZHOU, University of Macau, China XIAOCHUN CAO, Sun Yat-sen University, China 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 itsben- eficial 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. CCS Concepts: • Computing methodologies ! Computer vision representations; • Mathematics of computing ! Functional analysis; Numerical analysis; • General and reference ! Surveys and overviews. Additional Key Words and Phrases: pattern recognition, image representation, orthogonal moments, geometric invariance, fast computation 1 INTRODUCTION In the mid-twentieth century, American mathematician Claude Elwood Shannon published his paper A mathematical theory of communication [1], which marked the creation of information theory. As a landmark contribution, information theory is the theoretical foundation of information storage, processing, and transmission in modern computer systems. On the other hand, it also dictates that the raw signal (i.e., digital data) of multimedia (e.g., image) is not semantic in nature.
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