Harris Corner Detection Algorithm Implementation in MATLAB

This is a MATLAB implementation of the Harris corner detection algorithm. The program detects corner points in images and marks their locations with visual indicators. Harris corner detection is a fundamental computer vision algorithm that identifies significant corner points in images, which are crucial for feature extraction and matching tasks.

The implementation involves several key computational steps: 1. Image gradient computation using Sobel operators to calculate Ix and Iy derivatives 2. Structure tensor calculation involving Ix², Iy², and Ix·Iy products 3. Gaussian smoothing of the tensor components to reduce noise sensitivity 4. Corner response calculation using the Harris criterion: R = det(M) - k·trace(M)² 5. Non-maximum suppression to identify local maxima in the response map 6. Thresholding to select significant corners based on response strength

The code utilizes MATLAB's image processing capabilities including convolution operations for gradient computation and Gaussian filtering. Key functions employed include imfilter for spatial filtering, conv2 for 2D convolution, and colfilt for neighborhood processing during non-maximum suppression.

This implementation helps demonstrate the practical application of Harris corner detection and provides insights into the algorithm's parameter tuning, such as adjusting the sensitivity factor k and threshold values for optimal corner detection results across different image types.