SIFT Feature Extraction Algorithm Implementation

SIFT (Scale-Invariant Feature Transform) is a classic feature extraction algorithm designed to detect keypoints and generate local feature descriptors from images. The algorithm operates through several key stages: first, it constructs a scale space using Gaussian filters to identify scale-invariant extremum points. Then, it precisely localizes keypoints by eliminating low-contrast points and edge responses. The core implementation typically involves computing Difference-of-Gaussian (DoG) pyramids to detect stable keypoints across different scales.

After keypoint localization, the SIFT algorithm generates feature descriptors by analyzing local image gradients. This involves computing orientation histograms for regions around each keypoint, creating 128-dimensional vectors that capture distinctive patch characteristics. The implementation generally uses weighted gradient magnitude calculations and orientation binning to achieve rotation invariance.

Widely applied in computer vision, image processing, and pattern recognition, SIFT serves as fundamental knowledge for image understanding. Typical code implementations include functions for scale-space construction, keypoint detection, orientation assignment, and descriptor generation, often optimized using techniques like histogram interpolation and descriptor normalization.