Detecting Corners

Source code Author Update time

Corner Detection, which is a subset of Interest Point Detection, tries to detect points in the image which have a well-defined position and can be robustly detected in multiple images of the same scene. Very often, these points lie along the corners or edges of objects in the image - hence the name.

Corner detection is useful in several computer vision tasks - such as Image Registration, Motion Detection and Panaroma Stitching. This is because if the locations of the same points are known in two different images, it gives a reference to align those images. Corners, with their well-defined positions serve as good candidates for such points.

using Images, TestImages
img = Gray.(testimage("house"))

We use this image of a house, with numerous edges and corners.

The imcorner function can be used to detect corners in the image - and it returns an array of booleans, where a true value denotes that the corresponding pixel may be a corner. We use this to mark those pixels in red on a copy of the image.

corners = imcorner(img)
img_copy = RGB.(img)
img_copy[corners] .= RGB(1.0, 0.0, 0.0)

As you can see, several points which lie in the interior of the object (the house) have also been detected as corners. We can fix this by specifying a higher threshold percentile.

corners = imcorner(img, Percentile(98.5))
img_copy2 = RGB.(img)
img_copy2[corners] .= RGB(1.0, 0.0, 0.0)

This seems much better. A detailed documentation of the function parameters can be found in the documentation of imcorner.

Internally, imcorner uses one of three algorithms: Harris, Shi Tomasi or Kitchen Rosenfield to detect corners. Which one to use can be specified using the method parameter to imcorner. Each algorithm also has a separate method.

detection_methods = [harris, shi_tomasi, kitchen_rosenfeld]
img_copies = [RGB.(img) for i in 1:length(detection_methods)]
for i in 1:length(detection_methods)
    corners = imcorner(img, Percentile(98.5); method=detection_methods[i])
    img_copies[i] = RGB.(img)
    img_copies[i][corners] .= RGB(1.0, 0.0, 0.0)
mosaicview(img_copies; nrow=1)