Ehab Essa's Technical Blog: Corner detection

Corner detection is an important task in various computer vision and image-understanding systems . Corner detection should satisfy a number of important criteria:

All the true corners should be detected
No false corners should be detected
Corner points should be well localized
Corner detector should be robust with respect to noise
Corner detector should be efficient

Localization refers to how accurately the position of a corner is found. This is critical in applications requiring the precise alignment of multiple images (for example, in registration of medical images).

Applications of Corner Detectors

The use of interest points (and thus corner detectors) to find corresponding points across multiple images is a key step in many image processing and computer vision applications. Some of the most notable examples are:

stereo matching
image registration (of particular importance in medical imaging)
stitching of panoramic photographs
object detection/recognition
motion tracking
robot navigation

Figure shows part of a hypothetical system to illustrate how a corner detector might be used in an automated assembly line. This assembly line fills triangle gift boxes with four different chocolates. However, the boxes must be positioned properly on the conveyor belt to ensure the chocolates are packed properly into the boxes. An overhead camera is used to capture a picture of each box as it passes under it and a computer compares it to a stored image of a properly aligned box. By finding the corners of each image, how much the box needs to be rotated can easily be computed.

Evaluating and Comparing Corner Detectors
Each of the corner detectors are evaluated on an artificial test image containing different corner types and two real world images

The image contains many corner types (L-Junction, Y-Junction, T-Junction, Arrow-Junction, and X-Junction )