Unified multi-scale image corner detection
Corners provide strong evidence for the location of objects. Therefore, multi-scale corner
detection is very important in image processing, as it provides a sound balance between noise
removal and preserving detail. The robustness of corner detection has been used in many
existing applications in object recognition and interpretation. This thesis aims to analyse and
design a multi-scale corner detector.
A simple gradient expression in scale-space that describes the V-, T- and X-type corners in
a universal model is defined. We also describe a corner detector, based on Moments of the
Gradient in Scale-space (MoGS). The response of this detector is proportional to the edge
intensity difference and the sine of the aperture of the corner. The localisation of this
algorithm for V- and X-type corners is invariant to scales. The computation of corner
attributes for edge orientation, aperture, contrast, corner type and size are evaluated.
This algorithm and other corner detectors are evaluated using synthetic and natural images.
The results show that the MoGS operator is superior to the Plessey, Kitchen and SUSAN
(extended to consider scale) operators in corner detection and localization in scale-space.