Extracting Salient Curves from Images: An Analysis of the Saliency Network
International Journal of Computer Vision
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In this paper we present an approach to perceptual organization and attention based on Curved Inertia Frames (C.I.F.), a novel definition of ``curved axis of inertia'''' tolerant to noisy and spurious data. The definition is useful because it can find frames that correspond to {\it large, smooth, convex, symmetric and central parts}. It is novel because it is global and can detect curved axes. We discuss briefly the relation to human perception, the recognition of non-rigid objects, shape description, and extensions to finding "features", inside/outside relations, and long- smooth ridges in arbitrary surfaces.