Abstract

When we look at images, certain salient structures often attract our immediate attention, without requiring a systematic scan of the entire image. In subsequent stages, processing resources can be allocated preferentially to these salient structures. In many cases this saJiency is a property of the structure as a whole, i.e., parts of the structure are not salient in isolation. In this paper we present a saliency measure based on cur- vature and curvature variation. The structures this measure emphasizes are also salient in human perception, and they often correspond to objects of interest in the image. We present a method for computing the sallehey by a simple iterative scheme, using a uniform network of locally connected processing elements. The network uses an optimization approach to produce a "saliency map" which is a representation of the image emphasizing salient locations. The main.properties of the network are: (i) the computations are simple and local, (ii) globally salient structures emerge with a small number of iterations (iii) as a by-product of the computation contours are smoothed, and gaps are filled-in.

Citations