Combined invariants to blur and rotation using Zernike moment descriptors

Hongqing Zhu; Min Liu; Hanjie Ji; Yu Li

doi:10.1007/s10044-009-0159-9

ScienceGate Book Chapters

JOURNAL ARTICLE

Combined invariants to blur and rotation using Zernike moment descriptors

Hongqing Zhu Min Liu Hanjie Ji Yu Li

Year: 2009 Journal: Pattern Analysis and Applications Vol: 13 (3)Pages: 309-319 Publisher: Springer Science+Business Media

DOI: 10.1007/s10044-009-0159-9

Get Full-Text PDF Get Analytical Report

Keywords:

Zernike polynomials Velocity Moments Invariant (physics) Artificial intelligence Legendre polynomials Mathematics Computer vision Redundancy (engineering) Rotation (mathematics) Robustness (evolution) Motion blur Pattern recognition (psychology) Computer science Mathematical analysis Image (mathematics) Optics Physics

Metrics

Cited By

2.48

FWCI (Field Weighted Citation Impact)

Refs

0.92

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Image Retrieval and Classification Techniques

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Image Processing Techniques and Applications

Physical Sciences → Engineering → Media Technology

Advanced Image and Video Retrieval Techniques

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Combined invariants to blur and rotation using Zernike moment descriptors

Metrics

Citation History

Topics

Related Documents

Combined invariants to blur and rotation using Zernike moment descriptors

Pseudo-Zernike moment invariants to blur degradation and similarity transformation

Combined Invariants to Similarity Transformation and to Blur Using Orthogonal Zernike Moments

Shape redognition using zernike moment invariants

Super resolution image reconstruction using weighted combined Pseudo-Zernike moment invariants