Results 1  10
of
210
Stochastic Completion Fields: A Neural Model of Illusory Contour Shape and Salience
 Neural Computation
, 1995
"... 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 ..."
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Cited by 210 (14 self)
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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 vectorfield 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...
Fragmentbased image completion
 ACM TRANS. ON GRAPHICS. SPECIAL ISSUE: PROC. OF ACM SIGGRAPH
, 2003
"... We present a new method for completing missing parts caused by the removal of foreground or background elements from an image. Our goal is to synthesize a complete, visually plausible and coherent image. The visible parts of the image serve as a training set to infer the unknown parts. Our method it ..."
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Cited by 130 (4 self)
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We present a new method for completing missing parts caused by the removal of foreground or background elements from an image. Our goal is to synthesize a complete, visually plausible and coherent image. The visible parts of the image serve as a training set to infer the unknown parts. Our method iteratively approximates the unknown regions and composites adaptive image fragments into the image. Values of an inverse matte are used to compute a confidence map and a level set that direct an incremental traversal within the unknown area from high to low confidence. In each step, guided by a fast smooth approximation, an image fragment is selected from the most similar and frequent examples. As the selected fragments are composited, their likelihood increases along with the mean confidence of the image, until reaching a complete image. We demonstrate our method by completion of photographs and paintings.
A Neural Model of Contour Integration in the Primary Visual Cortex
 Neural Computation
, 1998
"... Experimental observations suggest that contour integration may take place in V1. However, there has yet to be a model of contour integration that only uses known V1 elements, operations, and connection patterns. This paper introduces such a model, using orientation selective cells, local cortical ci ..."
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Cited by 124 (5 self)
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Experimental observations suggest that contour integration may take place in V1. However, there has yet to be a model of contour integration that only uses known V1 elements, operations, and connection patterns. This paper introduces such a model, using orientation selective cells, local cortical circuits, and horizontal intracortical connections. The model is composed of recurrently connected excitatory neurons and inhibitory interneurons, receiving visual input via oriented receptive fields resembling those found in primary visual cortex. Intracortical interactions modify initial activity patterns from input, selectively amplifying the activities of edges that form smooth contours in the image. The neural activities produced by such interactions are oscillatory and edge segments within a contour oscillate in synchrony. It is shown analytically and empirically that the extent of contour enhancement and neural synchrony increases with the smoothness, length, and closure of contours, a...
Self Organization in Vision: Stochastic Clustering for Image Segmentation, Perceptual Grouping, and Image Database Organization
, 2001
"... We present a stochastic clustering algorithm which uses pairwise similarity of elements, and show how it can be used to address various problems in computer vision, including the lowlevel image segmentation, midlevel perceptual grouping, and highlevel image database organization. The clustering p ..."
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Cited by 89 (4 self)
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We present a stochastic clustering algorithm which uses pairwise similarity of elements, and show how it can be used to address various problems in computer vision, including the lowlevel image segmentation, midlevel perceptual grouping, and highlevel image database organization. The clustering problem is viewed as a graph partitioning problem, where nodes represent data elements and the weights of the edges represent pairwise similarities. We generate samples of cuts in this graph, by using Karger's contraction algorithm, and compute an "average" cut which provides the basis for our solution to the clustering problem. The stochastic nature of our method makes it robust against noise, including accidental edges and small spurious clusters. The complexity of our algorithm is very low: O(E log&sup2; N) for N objects, E similarity relations and a fixed accuracy level. In addition, and without additional computational cost, our algorithm provides a hierarchy of nested partitions. We demonstrate the superiority of our method for image segmentation on a few synthetic and real images, B&W and color. Our other examples include the concatenation of edges in a cluttered scene (perceptual grouping), and the organization of an image database for the purpose of multiview 3D object recognition.
Globally optimal regions and boundaries as minimum ratio weight cycles
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2001
"... We describe a new form of energy functional for the modelling and identification of regions in images. The energy is defined on the space of boundaries in the image domain, and can incorporate very general combinations of modelling information both from the boundary (intensity gradients,...), and ..."
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Cited by 84 (2 self)
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We describe a new form of energy functional for the modelling and identification of regions in images. The energy is defined on the space of boundaries in the image domain, and can incorporate very general combinations of modelling information both from the boundary (intensity gradients,...), and from the interior of the region (texture, homogeneity,. We describe two polynomialtime digraph algorithms for finding the global minima of this energy. One of the algorithms is completely general, minimizing the functional for any choice of modelling information. It runs in a few seconds on a 256 × 256 image. The other algorithm applies to a subclass of functionals, but has the advantage of being extremely parallelizable. Neither algorithm requires initialization.
Segmentation of Multiple Salient Closed Contours from Real Images
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2003
"... Using a saliency measure based on the global property of contour closure, we have developed a segmentation method which identifies smooth closed contours bounding objects of unknown shape in real images. The saliency measure incorporates the Gestalt principles of proximity and good continuity that ..."
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Cited by 83 (1 self)
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Using a saliency measure based on the global property of contour closure, we have developed a segmentation method which identifies smooth closed contours bounding objects of unknown shape in real images. The saliency measure incorporates the Gestalt principles of proximity and good continuity that previous methods have also exploited. Unlike previous methods, we incorporate contour closure by finding the eigenvector with the largest positive real eigenvalue of a transition matrix for aMarkov process whereedges from the image serve as states. Element i; j of the transition matrix is the conditional probability that a contour which contains edge j will also contain edge i. In this paper, we show how the saliency measure, defined for individual edges, can be used to derive a saliency relation, defined for pairs of edges, and further show that stronglyconnected components of the graph representing the saliency relation correspond to smooth closed contours in the image. Finally, we report for the first time, results on large real images for which segmentation takes an average of about 10 seconds per object on a generalpurpose workstation.
Supervised Learning of Large Perceptual Organization: Graph Spectral Partitioning and Learning Automata
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2000
"... this article, please send email to: tpami@computer.org, and reference IEEECS Log Number 107780 ..."
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Cited by 74 (6 self)
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this article, please send email to: tpami@computer.org, and reference IEEECS Log Number 107780
Meaningful Alignments
, 1999
"... We propose a method for detecting geometric structures in an image, without any a priori information. Roughly speaking, we say that an observed geometric event is "meaningful" if the expectation of its occurences would be very small in a random image. We discuss the apories of this definit ..."
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Cited by 65 (14 self)
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We propose a method for detecting geometric structures in an image, without any a priori information. Roughly speaking, we say that an observed geometric event is "meaningful" if the expectation of its occurences would be very small in a random image. We discuss the apories of this definition, solve several of them by introducing "maximal meaningful events" and analyzing their structure. This methodology is applied to the detection of alignments in images.
Completion energies and scale
, 2000
"... 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 met ..."
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Cited by 62 (6 self)
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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.
Salient Closed Boundary Extraction with Ratio Contour
 IEEE Trans. on Pattern Analysis and Machine Intelligence
, 2005
"... We present ratio contour, a novel graphbased method for extracting salient closed boundaries from noisy images. This method operates on a set of boundary fragments that are produced by edge detection. Boundary extraction identifies a subset of these fragments and connects them sequentially to for ..."
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Cited by 61 (16 self)
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We present ratio contour, a novel graphbased method for extracting salient closed boundaries from noisy images. This method operates on a set of boundary fragments that are produced by edge detection. Boundary extraction identifies a subset of these fragments and connects them sequentially to form a closed boundary with the largest saliency. We encode the Gestalt laws of proximity and continuity in a novel boundarysaliency measure based on the relative gap length and average curvature when connecting fragments to form a closed boundary. This new measure attempts to remove a possible bias toward short boundaries. We present a polynomialtime algorithm for finding the mostsalient closed boundary. We also present supplementary preprocessing steps that facilitate the application of ratio contour to real images. We compare ratio contour to two closely related methods for extracting closed boundaries: Elder and Zucker's method based on the shortestpath algorithm and Williams and Thornber's method based on spectral analysis and a stronglyconnectedcomponents algorithm. This comparison involves both theoretic analysis and experimental evaluation on both synthesized data and real images.