Combined invariants to blur and rotation using Zernike moment descriptors

• Published in: Pattern analysis and applications : PAA Vol. 13; no. 3; pp. 309 - 319
• Main Authors: Zhu, Hongqing, Liu, Min, Ji, Hanjie, Li, Yu
• Format: Journal Article
• Language: English
• Published: London Springer-Verlag 01.08.2010; Springer
• Subjects: Applied sciences; Artificial intelligence; Computer Science; Computer science; control theory; systems; Exact sciences and technology; Pattern Recognition; Pattern recognition. Digital image processing. Computational geometry; Theoretical Advances; Zernike moments; Rotation invariants; Radial moments; Pattern recognition; Blur invariants; Classification; Computer vision; Image processing; Redundancy; Object recognition; Blurred image; Zernike polynomial; Legendre polynomial; Noisy image; Moment invariant; Robustness

Moment invariants that are not affected by geometric transform have been utilized as pattern features in a number of applications. But in most cases, images are processed subject to blur degradations. The traditional blur invariant sets were constructed using geometric moments, central moments or co...