Toward robust moment invariants for image registration

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.