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

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, and definition extension. 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 realworld applications.

No-reference stereoscopic video quality assessment based on Tchebichef moment

“…The low-order moments of image reflect the overall intensity information of the image with strong robustness 21 . Therefore, we compute the low-order moments of keyframes to extract the video spatial features.…”