A snake is an energy-minimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines and edges. Snakes are active contour models: they lock onto nearby edges, localizing them accurately. Scale-space continuation can be used to enlarge the cap-ture region surrounding a feature. Snakes provide a unified account of a number of visual problems, in-cluding detection of edges, lines, and subjective contours; motion tracking; and stereo matching. We have used snakes successfully for interactive interpretation, in which user-imposed constraint forces guide the snake near features of interest.

In this paper we introduce a novel representation of the significant changes in curvature along the bounding contour of planar shape. Ve call the representation the curvature primgl ,sketch. We describe an implemented algorithn that computes the curvature primal sketch and illustra. te its performance on a set of tool shapes. The curvature primal sketch derites ils name from the close analogy to the primal sketch representation advocated ty Mart for descri.bi!)g significant intensity changes. We define a set of primitive parameterized curvature discontinuities, and derive expressions for their convolutions vith the first and second derivatives ot' a Gaussian. The convolved primitives, sorted according to the scale at 'which they are detected, provide us with a multi-scaled interpretation of the'contoar of a shape.

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 points are positions and orientations in the image plane (i.e., the space which one can reasonably assume is represented by neurons of the mammalian visual cortex). Our model does not employ numerical relaxation or other explicit minimization, but instead relies on the fact that the probability that a particle following a random walk will pass through a given position and orientation on a path joining two boundary fragments can be computed directly as the product of two vector-field convolutions. We show that for the random walk we define, the maximum likelihood paths are curves of least energy, that is, on average, random walks follow paths commonly assumed to model the shape of illusory co...

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.

Here we search fi)r the curve which has the smallest integral of the square of curvature, while passing through two given points with given orientation. This is the true shape of a spline used in lofting. In computer-aided design, curves have been sought which maximize "smoothness". The curve discussed here is the one arising in this way from a commonly used measure of smoothness. The human visual system may use such a curve when it constructs a subjective contour.

We have studied the mechanism of contour perception by recording from neurons in the visual cortex of alert rhesus monkeys. In order to assess the relationship between neural signals and perception, we compared the responses to edges and lines with the responses to patterns in which human observers perceive a contour where no line or edge is given (anomalous contour), such as the border between gratings of thin lines offset by half a cycle. With only one exception out of 60, orientation-selective neurons in area Vl did not signal the anomalous contour. Many neurons failed to re-spond to this stimulus at all, others responded according to the orientation of the grating lines. In area V2, 45 of 103 neurons (44%) signaled the orientation of the anomalous contour. Sixteen did so without signaling the orientation of the inducing lines. Some responded better to anomalous

The detection of smooth curves in images and their completion over gaps are two important problems in perceptual grouping. In this study, we examine the notion of completion energy of curve elements, showing, and exploiting its intrinsic dependence on length and width scales. We introduce a fast method for computing the most likelycompletion between two elements, by developing novel analytic approximations and a fast numerical procedure for computing the curve of least energy. We then use our newlydeveloped energies to find the most likelycompletions in images through a generalized summation of induction fields. This is done through multiscale procedures, i.e., separate processing at different scales with some interscale interactions. Such procedures allow the summation of all induction fields to be done in a total of only O(N log N) operations, where N is the number of pixels in the image. More important, such procedures yield a more realistic dependence of the induction field on the length and width scales: The field of a long element is verydifferent from the sum of the fields of its composing short segments.

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Researchers in computer vision have primarily studied the problem of visual reconstruction of environmental structure that is plainly visible. In this thesis, the conventional goals of visual reconstruction are generalized to include both visible and occluded forward facing surfaces. This larger fraction of the environment is termed the anterior surfaces. Because multiple anterior surface neighborhoods project onto a single image neighborhood wherever surfaces overlap, surface neighborhoods and image neighborhoods are not guaranteed to be in one-to-one correspondence, as conventional "shape-from" methods assume. The result is that the topology of threedimensional scene structure can no longer be taken for granted, but must be inferred from evidence...

The Saliency Network proposed by Shashua and Ullman (1988) is a well-known approach to the problem of extracting salient curves from images while performing gap completion. This paper analyzes the Saliency Network. The Saliency Network is attractive for several reasons. First, the network generally prefers long and smooth curves over short or wiggly ones. While computing saliencies, the network also fills in gaps with smooth completions and tolerates noise. Finally, the network is locally connected, and its size is proportional to the size of the image. Nevertheless, our analysis reveals certain weaknesses with the method. In particular, we show cases in which the most salient element does not lie on the perceptually most salient curve. Furthermore, in some cases the saliency measure changes its preferences when curves are scaled uniformly. Also, we show that for certain fragmented curves the measure prefers large gaps over a few small gaps of the same total size. In addition, we analyze the time complexity required by the method. We show that the number of steps required for convergence in serial implementations is quadratic in the size of the network, and in parallel implementations is linear in the size of the network. We discuss problems due to coarse sampling of the range of possible orientations. Finally, we consider the possibility of using the Saliency Network for grouping. We show that the Saliency Network recovers the most salient curve efficiently, but it has problems with identifying any salient curve other than the most salient one.