Pattern Recognition Methods for Object Boundary Detection

Boundary extraction is a data representation problem: image features are segmented and approximated by a parametric curve or a sequence of model points. However, the use of classic Pattern Recognition methods in boundary detection is unusual when compared with more popular approaches, e.g., active contours. This can be partially explained by their inability to separate boundary edges from other image strokes. This paper presents modified versions of several clustering and neural networks algorithms (c-means, fuzzy c-means, Kohonen maps, elastic nets), enhanced with dynamic data segmentation capabilities. This is achieved by using a noise model. The noise model consists of a virtual unit equidistant of all data points, which can be geometrically interpreted as a noise plane parallel to the image plane. The proposed technique extends the unified framework recently proposed by Abrantes and Marques [1] in the context of edge linking with constrained clustering techniques. Results ar