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Computing Contour Closure
 In Proc. 4th European Conference on Computer Vision
, 1996
"... . Existing methods for grouping edges on the basis of local smoothness measures fail to compute complete contours in natural images: it appears that a stronger global constraint is required. Motivated by growing evidence that the human visual system exploits contour closure for the purposes of p ..."
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Cited by 109 (11 self)
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. Existing methods for grouping edges on the basis of local smoothness measures fail to compute complete contours in natural images: it appears that a stronger global constraint is required. Motivated by growing evidence that the human visual system exploits contour closure for the purposes of perceptual grouping [6, 7, 14, 15, 25], we present an algorithm for computing highly closed bounding contours from images. Unlike previous algorithms [11, 18, 26], no restrictions are placed on the type of structure bounded or its shape. Contours are represented locally by tangent vectors, augmented by image intensity estimates. A Bayesian model is developed for the likelihood that two tangent vectors form contiguous components of the same contour. Based on this model, a sparselyconnected graph is constructed, and the problem of computing closed contours is posed as the computation of shortestpath cycles in this graph. We show that simple tangent cycles can be efficiently computed ...
Ecological statistics of Gestalt laws for the perceptual organization of contours
, 2002
"... Although numerous studies have measured the strength of visual grouping cues for controlled psychophysical stimuli, little is known about the statistical utility of these various cues for natural images. In this study, we conducted eFperiments in which human participants trace perceived contours in ..."
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Cited by 100 (6 self)
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Although numerous studies have measured the strength of visual grouping cues for controlled psychophysical stimuli, little is known about the statistical utility of these various cues for natural images. In this study, we conducted eFperiments in which human participants trace perceived contours in natural images. These contours are automatically mapped to seGuences of discrete tangent elements detected in the image. By eFamining relational properties between pairs of successive tangents on these traced curves, and between randomly selected pairs of tangents, we are able to estimate the likelihood distributions reGuired to construct an optimal Bayesian model for contour grouping. We employed this novel methodology to investigate the inferential power of three classical Gestalt cues for contour groupingJ proFimity, good continuation, and luminance similarity. The study yielded a number of important resultsJ K1M these cues, when appropriately defined, are approFimately uncorrelated, suggesting a simple factorial model for statistical inferenceN K2M moderate imagetoimage variation of the statistics indicates the utility of general probabilistic models for perceptual organiQationN KRM these cues differ greatly in their inferential power, proFimity being by far the most powerfulN and KSM statistical modeling of the proFimity cue indicates a scaleinvariant power law in close agreement with prior psychophysics.
Contour Grouping with Prior Models
 IEEE Trans. Pattern Analysis and Machine Intelligence
, 2003
"... Abstract—Conventional approaches to perceptual grouping assume little specific knowledge about the object(s) of interest. However, there are many applications in which such knowledge is available and useful. Here, we address the problem of finding the bounding contour of an object in an image when s ..."
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Cited by 34 (8 self)
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Abstract—Conventional approaches to perceptual grouping assume little specific knowledge about the object(s) of interest. However, there are many applications in which such knowledge is available and useful. Here, we address the problem of finding the bounding contour of an object in an image when some prior knowledge about the object is available. We introduce a framework for combining prior probabilistic knowledge of the appearance of the object with probabilistic models for contour grouping. A constructive search technique is used to compute candidate closed object boundaries, which are then evaluated by combining figure, ground, and prior probabilities to compute the maximum a posteriori estimate. A significant advantage of our formulation is that it rigorously combines probabilistic local cues with important global constraints such as simplicity (no selfintersections), closure, completeness, and nontrivial scale priors. We apply this approach to the problem of computing exact lake boundaries from satellite imagery, given approximate prior knowledge from an existing digital database. We quantitatively evaluate the performance of our algorithm and find that it exceeds the performance of human mapping experts and a competing active contour approach, even with relatively weak prior knowledge. While the priors may be taskspecific, the approach is general, as we demonstrate by applying it to a completely different problem: the computation of human skin boundaries in natural imagery.
Finding boundaries in natural images: A new method using point descriptors and area completion
 In Proc. 5th Euro. Conf. Computer Vision
, 1998
"... Abstract. We develop an approach to image segmentation for natural scenes containing image texture. One general methodology which shows promise for solving this problem is to characterize textured regions via their responses to a set of lters. However, this approach brings with it many openquestions ..."
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Cited by 12 (7 self)
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Abstract. We develop an approach to image segmentation for natural scenes containing image texture. One general methodology which shows promise for solving this problem is to characterize textured regions via their responses to a set of lters. However, this approach brings with it many openquestions, including how to combine texture and intensity information into a common descriptor and how to deal with the fact that lter responses inside textured regions are generally spatially inhomogeneous. Our goal in this paper is to introduce two new ideas which address these open questions and to demonstrate the application of these ideas to the segmentation of natural images. The rst idea consists of anovel means of describing points in natural images and an associated distance function for comparing these descriptors. This distance function is aided in textured regions by the use of the second idea, a new process introduced here which we have termed area completion. Experimental segmentation results which incorporate our proposed approach into the Normalized Cut framework of Shi and Malik are provided for a variety of natural images. 1