What are the drawbacks of Harris corner detector? [closed]

The one big drawback of harris corner detection is that we need to set different threshold values for every image in order to detect the most prominent interest points. If the threshold value used is too low, then the algorithm may end up detecting large amount of points with noisy image data.

1. Which is the main advantage of the Harris detector over the Moravec detector?
2. How is the fast detector different from the Harris corner detector?
3. How does Harris detector work?
4. What is corner detector in image processing?

Which is the main advantage of the Harris detector over the Moravec detector?

Moravec only considered shifts in discrete 45 degree angles whereas Harris considered all directions. Harris detector has proved to be more accurate in distinguishing between edges and corners.

How is the fast detector different from the Harris corner detector?

The Harris algorithm uses more hardware resources than the FAST algorithm but can detect corners that the FAST algorithm might not find.

How does Harris detector work?

The Harris corner detector works by taking horizontal and vertical derivatives of the image and looking for areas where both are high, this is quantified by the Harris corner descriptor which is defined in our case as the matrix �and the descriptor is .

What is corner detector in image processing?

Corner detection works on the principle that if you place a small window over an image, if that window is placed on a corner then if it is moved in any direction there will be a large change in intensity.