Corner detection via trilateral filtering of chain codes

Abstract

Contours are the most common feature used in the shape detection and recognition applications. Contours in digital images constitute of consecutive pixels, thus they can be represented with chain codes by encoding the relative positions of the pixels forming that contour. Sharp transitions on chain coded contour information correspond to the corners or notches of the 2D shape enclosed by that contour. Moreover, useful information about geometric structures and orientations of the shapes can be gathered by examining the distributions and moments of the chain codes. Despite their profitable properties, chain codes are not robust against the rotation. Because contours are exposed to quantization while they are being digitized, same geometric shape may have different chain code representations. Another downside about the chain codes is the difficulty of their extraction from real images in an efficient manner. In this study, we propose a novel real-time corner detection method by extracting the chain codes by Edge Drawing (ED) method and after processing them with the trilateral filtering.