Feature Point Extraction Using Moravec Operator with Correlation-Based Matching

In this process, the Moravec operator can be employed to extract feature points from images. These feature points serve as distinctive landmarks that help characterize image content more effectively. The Moravec algorithm works by computing intensity variations in multiple directions (typically horizontal, vertical, and two diagonals) within a local window, selecting the minimum variance as the corner response value. Points exceeding a predefined threshold are identified as features. Following feature extraction, correlation-based matching techniques are applied to establish correspondences between feature points across different images. This matching process calculates similarity measures (such as normalized cross-correlation) between feature descriptors to identify corresponding points in target images. This methodology is fundamental in computer vision and image processing applications, enabling automated image recognition, classification tasks, and supporting various advanced technologies including autonomous driving systems and robotic vision. The implementation typically involves sliding window operations, variance calculations, non-maximum suppression for feature selection, and correlation coefficient computations for robust matching.