The Harris Corner Detector is an algorithm used in computer vision to identify corners or interest points in images. It works by analyzing the intensity changes in a small window around each pixel, determining where significant changes occur in multiple directions. This helps in detecting corners, which are important features for tasks like image matching and object recognition. Developed by Chris Harris and Mike Stephens in 1988, the detector is based on the concept of the autocorrelation matrix. It calculates a response function that highlights corner points, making it robust to noise and variations in lighting. This makes the Harris Corner Detector widely used in various applications, including robotics and image stitching.