Toward robust moment invariants for image registration

Abstract

We apply pattern recognition techniques to enhance the robustness of moment-invariants-based image classifiers. Moment invariants exhibit variations under transformations that do not preserve the original image function, such as geometrical transformations involving interpolation. Such variations degrade the performance of classifiers due to the errors in the nearest neighbor search stage. We propose the use of Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) to alleviate the variations and enhance the robustness of classification. We demonstrate the improved performance in image registration applications under spatial scaling and rotation transformations.

A New Approach of Local Feature Descriptors Using Moment Invariants

“…Although the object undergo 2D transformations (translation, scale, rotation and skew), the identification task remain invariance. Due to the promising result, moment invariants are further extended to new areas, such as hand gesture recognition, image registration, fingerprint verification, image retrieval and action classification (Almoosa et al, 2008;Chen et al, 2013;Costantini et al, 2011;Li et al, 2012).…”

Visual Tracking using Invariant Feature Descriptor

“…The presented set is able to eliminate the transformation factors, no matter in translation, scaling, reflection, skew and rotation. Since then, the research topic of moments function has been extensively explored for the past few decades (Almoosa et al, 2008;Chen et al, 2013;Costantini et al, 2011;Li et al, 2012). Every publication has reported its improved version of moments.…”