Pattern Recognition Methods for Object Boundary Detection
Arnaldo J. Abrantes, Jorge Salvador Marques · 1998
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 bound-ary 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 ver-sions of several clustering and neural networks algorithms (c-means, fuzzy c-means, Kohonen maps, elastic nets), enhanced with dynamic data segmen-tation 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 geomet-rically 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 tech-niques. Results are provided in the paper to illustrate the segmentation capa-bility of the novel methods in the analysis of images with undesired strokes (e.g., inside the object). It is concluded that the noise model proposed in this paper allows a widespread use of classic constrained clustering algorithms in the context of shape analysis. 1