The revised Fundamental Theorem of Moment Invariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition accuracy and user acceptance of pen interfaces
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The immediate usability of graffiti
Proceedings of the conference on Graphics interface '97
On the Accuracy of Zernike Moments for Image Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Sketched Symbol Recognition using Zernike Moments
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Online Handwritten Shape Recognition Using Segmental Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Moments and Moment Invariants in Pattern Recognition
Moments and Moment Invariants in Pattern Recognition
A Novel Shape Based Hierarchical Retrieval System for 2D Images
ARTCOM '10 Proceedings of the 2010 International Conference on Advances in Recent Technologies in Communication and Computing
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Pen based inputs are natural for human beings. A hand-drawn shape (symbol) can be used for various purposes, like, a command gesture, an input for authentication purpose, etc. Shape of a symbol is invariant to scale, translation, mirror-reflection and rotation of the symbol. Moments, like Zernike moments are often used to represent a symbol. Descriptors based on Zernike moments are rotation invariant, but since they are neither translation nor scale invariant, a normalization step as pre-processing is required. Apart from this, higher order Zernike moments are error prone. The present paper, proposes to use probability distributions of some local moments of lower order, as a representation scheme. Theoretically it is shown to possess all invariance properties. Experimentally, using the k-nearest neighbor classifier (with Kullback-Leibler distance), it is shown to perform better than Zernike moments based representation scheme.