TL;DR
This paper introduces a systematic formalism for computing geometric moment invariants in n-dimensional space, aiding pattern recognition by capturing object characteristics invariant to transformations.
Contribution
It presents a simple, systematic framework for calculating moment invariants across any dimensional space, enhancing pattern recognition techniques.
Findings
Provides a formalism for n-dimensional moment invariants
Simplifies computation of geometric moments
Enhances invariance to transformations in pattern recognition
Abstract
For more than half a century, moments have attracted lot ot interest in the pattern recognition community.The moments of a distribution (an object) provide several of its characteristics as center of gravity, orientation, disparity, volume. Moments can be used to define invariant characteristics to some transformations that an object can undergo, commonly called moment invariants. This work provides a simple and systematic formalism to compute geometric moment invariants in n-dimensional space.
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Video Analysis and Summarization · Image Retrieval and Classification Techniques