Complete Implementation Process of SIFT Algorithm

In this documentation, I will provide a detailed explanation of the complete implementation process of the SIFT algorithm using MATLAB. The SIFT (Scale-Invariant Feature Transform) algorithm serves as an excellent method for feature point extraction in image recognition applications. This algorithm effectively identifies key points within images and generates distinctive descriptors, playing a crucial role in areas such as image matching and object recognition. The MATLAB implementation follows the standard SIFT workflow: first detecting local extrema points in the image by building a Gaussian scale-space pyramid, then computing scale-space extrema to identify stable feature points. The implementation includes calculating gradient magnitudes and orientations in the neighborhood surrounding each feature point, followed by generating 128-dimensional feature descriptors using orientation histograms. These descriptors enable subsequent feature matching and recognition tasks. In the fields of image processing and computer vision, the SIFT algorithm has been widely adopted and has demonstrated excellent performance results. The code structure typically involves key functions for Gaussian pyramid construction, keypoint detection, orientation assignment, and descriptor generation, ensuring scale and rotation invariance for robust feature matching.