Extracting Image Invariant Moment Output

This article explores how to use MATLAB to extract invariant moments from images. Invariant moments represent a specialized image processing technique for extracting robust image features that remain unchanged under various transformations. The seven classic Hu invariant moments can effectively describe an image's geometric shape and spatial characteristics while being invariant to rotation, scale, and translation.

MATLAB provides efficient implementation for computing these seven invariant moment values rapidly through built-in image processing functions. The calculation typically involves preprocessing steps like image binarization, followed by computing central moments and normalizing them to achieve invariance. These moment values serve as powerful descriptors for image recognition and classification tasks, where they can be implemented using MATLAB's regionprops function or custom moment calculation algorithms. Additionally, invariant moments find applications in image matching and object tracking systems, making them essential for computer vision applications.

Mastering invariant moment extraction techniques holds significant importance for both research and practical applications in image processing. The implementation typically involves calculating raw moments using double summation over image coordinates, then deriving central moments by shifting to the centroid, and finally normalizing to obtain scale-invariant features. This article aims to help readers better understand invariant moment technology and apply it effectively in their MATLAB-based image processing projects, providing crucial support for advanced computer vision applications.