An automatic method for generating affine moment invariants

Authors:
Jin Liu;Deren Li;Wenbing Tao;Li Yan
Affiliations:
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Hongsan Luoyu Road #129, Wuhan 430079, PR China and School of Geodesy and Geomatics, Wuh ...;State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Hongsan Luoyu Road #129, Wuhan 430079, PR China;Service Computing Technology and System Laboratory of Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, China;School of Geodesy and Geomatics, Wuhan University, Hongsan Luoyu Road #129, Wuhan 430079, PR China
Venue:
Pattern Recognition Letters
Year:
2007

Citing 12
Cited 2

On Image Analysis by the Methods of Moments

IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant Image Recognition by Zernike Moments

IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine recognition of optically captured machine printed arabic text

Pattern Recognition
The revised Fundamental Theorem of Moment Invariants

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern recognition with moment invariants: a comparative study and new results

Pattern Recognition
Theory of algebraic invariants

Theory of algebraic invariants
Affine moment invariants: a new tool for character recognition

Pattern Recognition Letters
Generation of moment invariants and their uses for character recognition

Pattern Recognition Letters
Degraded Image Analysis: An Invariant Approach

IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching and normalization of affine deformed image from regular moments

Pattern Recognition Letters
Aircraft identification by moment invariants

IEEE Transactions on Computers
Image Normalization by Complex Moments

IEEE Transactions on Pattern Analysis and Machine Intelligence

Novel moment invariants for improved classification performance in computer vision applications

Pattern Recognition
Smart objects identification system for robotic surveillance

International Journal of Automation and Computing

Quantified Score

Hi-index	0.10

Visualization

Abstract

Affine moment invariants are important if one wants to recognize the surface of a plane in three dimensions when the orientation of the plane is not known beforehand and only two-dimensional information is available. The notion of generating function is introduced as a simple and straightforward way to derive various affine invariants. By this notion, we can get the explicit construction of much more affine moment invariants. Based on this conclusion, a large set of invariant polynomials can be generated automatically and immediately by the algorithm we have designed. These new affine moment invariants can be applied to recognize the image. Approaches in this paper will improve the practicability of affine invariants in object recognition applications.