Extracting Image Features Using 7 Hu Invariant Moments

Hu invariant moments represent a classical image feature extraction method that effectively describes the shape characteristics of objects within images. Based on geometric moment calculations, this method exhibits translation, scale, and rotation invariance, making it widely applicable in pattern recognition and computer vision domains.

The implementation process for Hu moment extraction in MATLAB primarily consists of the following steps: First, preprocess the image through binarization to emphasize target shapes; then calculate geometric moments including zero-order, first-order, and second-order moments; subsequently compute central moments based on geometric moments; finally derive the 7 Hu invariant moments using central moments. The algorithm leverages MATLAB's regionprops function for basic moment calculations and implements custom functions for central moment normalization and Hu moment derivation.

These seven invariant moments provide stable shape characterization suitable for tasks such as character recognition and object classification. MATLAB's powerful matrix operations enable efficient implementation, typically requiring only a few dozen lines of code to complete the entire feature extraction pipeline. Key functions involved include bwlabel for connected component analysis and moment calculations using double summation over image coordinates.

It's noteworthy that Hu moments perform well for global shape description but exhibit limited sensitivity to local detail variations. In practical applications, they are often combined with other feature extraction methods to improve recognition accuracy, such as incorporating texture features or deep learning descriptors.