Corner detection using Gabor filters

This study proposes a contour-based corner detector using the magnitude responses of the imaginary part of the Gabor filters on contours. Unlike the traditional contour-based methods that detect corners by analysing the shape of the edge contours and searching for local curvature maxima points on planar curves, the proposed corner detector combines the pixels of the edge contours and their corresponding grey-variation information. Firstly, edge contours are extracted from the original image using Canny edge detector. Secondly, the imaginary parts of the Gabor filters are used to smooth the pixels on the edge contours. At each edge pixel, the magnitude responses at each direction are normalised by their values and the sum of the normalised magnitude response at each direction is used to extract corners from edge contours. Thirdly, both the magnitude response threshold and the angle threshold are used to remove the weak or false corners. Finally, the proposed detector is compared with five state-of-the-art detectors on some grey-level images. The results from the experiment reveal that the proposed detector is more competitive with respect to detection accuracy, localisation accuracy, affine transforms and noise-robustness.