. In Proc. European Conf. Computer Vision, 1996, pp. 343--356, Cambridge, UK The problem of tracking curves in dense visual clutter is a challenging one. Trackers based on Kalman filters are of limited use; because they are based on Gaussian densities which are unimodal, they cannot represent

Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Problems associated with initialization and poor convergence to boundary concavities, however, have limited their utility. This paper presents a new

Reigning measures of psychological well-being have little theoretical grounding, despite an extensive literature on the contours of positive functioning. Aspects of well-being derived from this literature (i.e., self-acceptance, positive relations with others, autonomy, environmental mastery

The limitations of commonly used separable extensions of one-dimensional transforms, such as the Fourier and wavelet transforms, in capturing the geometry of image edges are well known. In this paper, we pursue a “true” two-dimensional transform that can capture the intrinsic geometrical structure

It is now accepted that the visual system integrates local orientation information across space to define spatial contours [Vision Research 33 (1993) 173]. More recently, it has been shown that similar integration occurs for the direction of local motion signals, in different parts of the visual

regions from sample textures and 2) “inpainting ” techniques for filling in small image gaps. The former has been demonstrated for “textures”—repeating twodimensional patterns with some stochasticity; the latter focus on linear “structures ” which can be thought of as one-dimensional patterns

along the curve, imposes a regularization effect like snakes. We explore the relation between the maximum curvature along the resulting contour and the potential generated from the image. The method is capable to close contours, given only one point on the objects’ boundary by using a topology

level algorithms (perfect contours, no noise, no fragmentation, etc.). Many researchers resort to using synthetic data as their input because of this weak link. Determining groups in a given set of points or edgels can be a very expensive computational task. Not only does one need to consider all

We describe an algorithm and representation level theory of illusory contour shape and salience. Unlike previous theories, our model is derived from a single assumption--- namely, that the prior probability distribution of boundary completion shape can be modeled by a random walk in a lattice whose

We propose a new rendering technique that produces 3-D images with enhanced visual comprehensibility. Shape fea-tures can be readily understood if certain geometric proper-ties are enhanced. To achieve this, we develop drawing algo-rithms for discontinuities, edges, contour lines, and curved