Harris-Corner-Detector

Harris Corner Detector is a corner detection operator that is commonly used in computer vision algorithms to extract corners and infer features of an image.

A corner is a point whose local neighborhood stands in two dominant and different edge directions. In other words, a corner can be interpreted as the junction of two edges, where an edge is a sudden change in image brightness. Corners are the important features in the image, and they are generally termed as interest points which are invariant to translation, rotation, and illumination.

The Harris Corner Detector algorithm in simple words is as follows :

STEP 1. It determines which windows (small image patches) produce very large variations in intensity when moved in both X and Y directions (i.e. gradients).

STEP 2. With each such window found, a score R is computed.

STEP 3. After applying a threshold to this score, important corners are selected and marked.

Take the gray-scale of the original image. Apply a Gaussian filter to smooth out any noise. Apply Sobel operator to find the x and y gradient values for every pixel in the grayscale image. For each pixel p in the grayscale image, consider a m*m window around it and compute the corner strength function. Call this its Harris value. Find all pixels that exceed a certain threshold and are the local maxima within a certain window (to prevent redundant dupes of features). Compute a feature descriptor of all such points.

For more all the algorithms used refer the Report file
For more details refer : https://iitmcvg.github.io/summer_school/Session3/