Introduction

Corners and junctions are essential 2D features of an image. The certain identification of these points provides important information in numerous Computer Vision applications such as stereo-vision, motion detection and scene analysis. They indicate the presence and position of objects, narrowing down the search problem and making high-level interpretation of images easier. To this end, it is also important to evaluate the orientation of the arms, i.e. , the edges which intersect as corners or junctions.

Most corner detectors are based on second order derivative schemes. Kitchen and Rosenfeld [ 6 ]compute the rate of change of gradient direction along an edge, multiplied by the gradient magnitude, as an estimation of the strength of a corner by using the following expression:

where x = (x, y), I( x ) is the original image, , , , and . Dreschler and Nagel [ 5 ], Zuniga and Haralick [ 7 ] adopted nearly equivalent approaches, all of them being sensitive to noise, because of the fact that derivatives amplify noise. It has been suggested that median corner detectors - that rely on the difference between an image and its median-filtered version - give better results than that of the Kitchen-Rosenfeld technique [ 1 ]. Other methods for corner detection include template matching (but a large set of templates is often necessary, making the search practically infeasible), generalised Hough transform (adapted to curved or chipped corners but with sensitivity and accuracy problems requiring a fine tuning) [ 2 ] and dissimilarity corner detectors (a fast and robust method that does not require image derivation, and therefore cannot provide edge information) [ 4 ].

Traditional corner detection does not tackle the problem of corner orientation. A common solution is to find the mean gradient orientation over a small neighbourhood of each estimated corner. This provides only the averaged orientation of the arms of the corner, with an accuracy less than 20 degrees [ 1 ]. These techniques are not well adapted to handling junctions.

In this paper we introduce a corner detector based on the analysis of local anisotropism [ 3 ] and identify corners as points of strong gradient intensity without a single dominant orientation. We then calculate the orientation of the arms of the corner or junction by measuring the likelihood of surrounding pixels being part of the corner structure.