We address the problem of obtaining dense surface information from a sparse set of 3D data in the presence of spurious noise samples. The input can be in the form of points, or points with an associated tangent or normal, allowing both position and direction to be corrupted by noise. Most approaches treat the problem as an interpolation problem, which is solved by fitting a surface such as a membrane or thin plate to minimize some function. We argue that these physical constraints are not sufficient, and propose to impose additional perceptual constraints such as good continuity and "cosurfacity". These constraints allow us to not only infer surfaces, but also to detect surface orientation discontinuities, as well as junctions, all at the same time. The approach imposes no restriction on genus, number of discontinuities, number of objects, and is noniterative. The result is in the form of three dense saliency maps for surfaces, intersections between surfaces (i.e., 3D curves), and 3D junctions, respectively. These saliency maps are then used to guide a "marching" process to generate a description (e.g., a triangulated mesh) making information about surfaces, space curves, and 3D junctions explicit. The traditional marching process needs to be refined as the polarity of the surface orientation is not necessarily locally consistent. These three maps are currently not integrated, and this is the topic of our ongoing research. We present results on a variety of computer-generated and real data, having varying curvature, of different genus, and multiple objects.

An attempt is made to solve the problem of imperfect data produced by state-of-the-art edge detectors through the implementation of laws of perceptual grouping, derived from psychology. A saliency-enhancing operator is introduced. It is capable of highlighting features (edges, junctions, etc.) which are considered important psychologically. It also infers features which are not detected by low-level detectors. It is shown how to extract salient curves and junctions and generate a description ranking these features by the likelihood of them occurring accidentally. The problem of illusory contours apparent in end-point formations is discussed. All operations are parameter-free, noniterative and are linear with the number of edges in the input image.>

Introduces saliency-enhancing operators capable of highlighting features which are considered perceptually relevant. One is able to extract salient curves and junctions and generate a description ranking these features by their likelihood of coming from the original scene. The authors suggest the global extension field as means of describing the behavior of a curve segment, in terms of its continuation. It is shown that a directional convolution of an edge image with the above field can produce useful descriptions. Other fields are also used in the same manner to produce similar results for domain-specific applications. The scheme is particularly useful and robust as a gap filler and in the presence of noise.>

An approach to stereo image compression based on disparity compensation is proposed and evaluated. The scheme is motivated by the suppression theory in human vision. A methodology for evaluating compressed stereo images is proposed. It is based on time measurements of depth perception tasks performed by human subjects. Subjects taking part in the experiment were exposed to displays of stereo images, some of which had been compressed, and were asked to judge the relative depth within each display as fast as possible. Decision times were measured and used as the major dependent variable. It was found that very deep compression of one of the images of a stereo pair does not interfere with the perception of depth in the stereo image.>