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
Digital Pattern Recognition by Moments
Journal of the ACM (JACM)
Extracting individual features from moments for Chinese writer identification
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Writer Identification By Writer's Invariants
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Establishing Handwriting Individuality Using Pattern Recognition Techniques
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Image analysis by moments
Analysis of Handwriting Individuality Using Word Features
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Off-line Handwriting Identification Using HMM Based Recognizers
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
A writer identification and verification system
Pattern Recognition Letters
Invariants Discretization for Individuality Representation in Handwritten Authorship
IWCF '08 Proceedings of the 2nd international workshop on Computational Forensics
Hi-index | 0.00 |
Past few years, a lot of research on moment functions have been explored in pattern recognition. Several new techniques have been investigated to improve conventional regular moment by proposing the scaling factor of geometrical function. In this paper, integrated scaling formulations of Aspect Invariant Moment and Higher Order Scaling Invariant with United Moment Invariant are presented in Writer Identification to seek the invarianceness of authorship or individuality of handwriting perseverance. Mathematical proving and results of computer simulations are included to verify the validity of the proposed technique in identifying eccentricity of the author in Writer Identification.