Results 1  10
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28
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 50 (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.
Untangling Cycles for Contour Grouping
"... We introduce a novel topological formulation for contour grouping. Our grouping criterion, called untangling cycles, exploits the inherent topological 1D structure of salient contours to extract them from the otherwise 2D image clutter. To define a measure for topological classification robust to cl ..."
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Cited by 40 (10 self)
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We introduce a novel topological formulation for contour grouping. Our grouping criterion, called untangling cycles, exploits the inherent topological 1D structure of salient contours to extract them from the otherwise 2D image clutter. To define a measure for topological classification robust to clutter and broken edges, we use a graph formulation instead of the standard computational topology. The key insight is that a pronounced 1D contour should have a clear ordering of edgels, to which all graph edges adhere, and no long range entanglements persist. Finding the contour grouping by optimizing these topological criteria is challenging. We introduce a novel concept of circular embedding to encode this combinatorial task. Our solution leads to computing the dominant complex eigenvectors/eigenvalues of the random walk matrix of the contour grouping graph. We demonstrate major improvements over stateoftheart approaches on challenging real images. 1.
The generalized A* architecture
 Journal of Artificial Intelligence Research
, 2007
"... We consider the problem of computing a lightest derivation of a global structure using a set of weighted rules. A large variety of inference problems in AI can be formulated in this framework. We generalize A * search and heuristics derived from abstractions to a broad class of lightest derivation p ..."
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Cited by 33 (6 self)
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We consider the problem of computing a lightest derivation of a global structure using a set of weighted rules. A large variety of inference problems in AI can be formulated in this framework. We generalize A * search and heuristics derived from abstractions to a broad class of lightest derivation problems. We also describe a new algorithm that searches for lightest derivations using a hierarchy of abstractions. Our generalization of A * gives a new algorithm for searching AND/OR graphs in a bottomup fashion. We discuss how the algorithms described here provide a general architecture for addressing the pipeline problem — the problem of passing information back and forth between various stages of processing in a perceptual system. We consider examples in computer vision and natural language processing. We apply the hierarchical search algorithm to the problem of estimating the boundaries of convex objects in grayscale images and compare it to other search methods. A second set of experiments demonstrate the use of a new compositional model for finding salient curves in images. 1.
Curve Finder Combining Perceptual Grouping and a Kalman Like Fitting
 In Proc. of ICCV'99
, 1999
"... We present an algorithm that extracts curves from a set of edgels within a specific class in a decreasing order of their "length". The algorithm inherits the perceptual grouping approaches. But, instead of using only local cues, a global constraint is imposed to each extracted subset of ed ..."
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Cited by 11 (0 self)
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We present an algorithm that extracts curves from a set of edgels within a specific class in a decreasing order of their "length". The algorithm inherits the perceptual grouping approaches. But, instead of using only local cues, a global constraint is imposed to each extracted subset of edgels, that the underlying curve belongs to a specific class. In order to reduce the complexity of the solution, we work with a linearly parameterized class of curves, function of one image coordinate. This allows, first, to use a recursive Kalman based fitting and, second, to cast the problem as an optimal path search in an directed graph. Experiments on finding lanemarkings on roads demonstrate that realtime processing is achievable.
Multiscale structural saliency for signature detection
 In Proc. IEEE Conf. Computer Vision and Pattern Recognition
, 2007
"... Detecting and segmenting freeform objects from cluttered backgrounds is a challenging problem in computer vision. Signature detection in document images is one classic example and as of yet no reasonable solutions have been presented. In this paper, we propose a novel multiscale approach to jointl ..."
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Cited by 6 (5 self)
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Detecting and segmenting freeform objects from cluttered backgrounds is a challenging problem in computer vision. Signature detection in document images is one classic example and as of yet no reasonable solutions have been presented. In this paper, we propose a novel multiscale approach to jointly detecting and segmenting signatures from documents with diverse layouts and complex backgrounds. Rather than focusing on local features that typically have large variations, our approach aims to capture the structural saliency of a signature by searching over multiple scales. This detection framework is general and computationally tractable. We present a saliency measure based on a signature production model that effectively quantifies the dynamic curvature of 2D contour fragments. Our evaluation using large real world collections of handwritten and machine printed documents demonstrates the effectiveness of this joint detection and segmentation approach. 1.
A Probabilistic Interpretation of the Saliency Network
 In ECCV00
, 2000
"... The calculation of salient structures is one of the early and basic ideas of perceptual organization in Computer Vision. Saliency algorithms aim to nd image curves, maximizing some deterministic quality measure which grows with the length of the curve, its smoothness, and its continuity. This n ..."
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Cited by 4 (0 self)
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The calculation of salient structures is one of the early and basic ideas of perceptual organization in Computer Vision. Saliency algorithms aim to nd image curves, maximizing some deterministic quality measure which grows with the length of the curve, its smoothness, and its continuity. This note proposes a modi ed saliency estimation mechanism, which is based on probabilistically speci ed grouping cues and on length estimation. In the context of the proposed method, the wellknown saliency mechanism, proposed by Shaashua and Ullman [SU88], may be interpreted as a process trying to detect the curve with maximal expected length.
Perceptual Organization of Visual Flows
, 2003
"... Locally parallel dense patterns visual flows define a perceptually coherent structure of particular significance to perceptual organization. Geometrically, it is argued that a proper way to investigate these structures requires the frame field approach from differential geometry, a study that lead ..."
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Cited by 4 (3 self)
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Locally parallel dense patterns visual flows define a perceptually coherent structure of particular significance to perceptual organization. Geometrically, it is argued that a proper way to investigate these structures requires the frame field approach from differential geometry, a study that leads to the notion of visual flow curvatures and to constraints on their mutual behavior. These curvatures are then used to develop a theory, and a rigorous model, of visual flow “good continuation ” that extends common terminology from Gestalt psychology and from computational studies of curves. The geometrical theory is then applied in three ways. Firstly, psychophysical exploration of the role of visual flow curvatures in human perception shows that sensitivity to these curvatures greatly affects orientationbased texture segmentation. Secondly, a contextual framework for the computation of coherent visual flows from images is developed and applied to texture, shading, and color analysis. Unlike existing approaches, the proposed framework is able to handle both sparse, dense, and multivalued data sets, while preserving line and point singularities and rejecting large scale nonflow structures. Lastly, the geometrical theory is linked to the functional organization of primary visual cortex to accurately predict the distribution of long range horizontal connections and to support their identification with those obtained mathematically.
Contour cut: identifying salient contours in images by solving a hermitian eigenvalue problem
 In Proc. IEEE Conf. on Comp. Vision and Patt. Recog
, 2011
"... The problem of finding onedimensional structures in images and videos can be formulated as a problem of searching for cycles in graphs. In [11], an untanglingcycle cost function was proposed for identifying persistent cycles in a weighted graph, corresponding to salient contours in an image. We ha ..."
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Cited by 3 (0 self)
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The problem of finding onedimensional structures in images and videos can be formulated as a problem of searching for cycles in graphs. In [11], an untanglingcycle cost function was proposed for identifying persistent cycles in a weighted graph, corresponding to salient contours in an image. We have analyzed their method and give two significant improvements. First, we generalize their cost function to a contour cut criterion and give a computational solution by solving a family of Hermitian eigenvalue problems. Second, we use the idea of a graph circulation, which ensures that each node has a balanced in and outflow and permits a natural randomwalk interpretation of our cost function. We show that our method finds far more accurate contours in images than [11]. Furthermore, we show that our method is robust to graph compression which allows us to accelerate the computation without loss of accuracy. 1.
Image Segmentation and Object Extraction Based on Geometric Features of Regions
, 1999
"... We propose a method for segmenting a color image into objectregions each of which corresponds to the projected region of each object in the scene onto an image plane. In conventional segmentation methods, it is not easy to extract an objectregion as one region. Our proposed method uses geometric f ..."
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Cited by 3 (0 self)
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We propose a method for segmenting a color image into objectregions each of which corresponds to the projected region of each object in the scene onto an image plane. In conventional segmentation methods, it is not easy to extract an objectregion as one region. Our proposed method uses geometric features of regions. At first, the image is segmented into small regions. Next, the geometric features such as inclusion, area ratio, smoothness, and continuity, are calculated for each region. Then the regions are merged together based on the geometric features. This merging enables us to obtain an objectregion even if the surface of the object is textured with a variety of reflectances; this isn't taken into account in conventional segmentation methods. We show experimental results demonstrating the effectiveness of the proposed method.
An user based framework for salient detail extraction
 ICME 2004
"... In this paper, we consider the interaction with salient details in the image i.e. points, lines, and regions. Interactive salient detail definition goes further than summarizing the image into a set of salient details since the saliency of details depends on the context, the application and the user ..."
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Cited by 2 (0 self)
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In this paper, we consider the interaction with salient details in the image i.e. points, lines, and regions. Interactive salient detail definition goes further than summarizing the image into a set of salient details since the saliency of details depends on the context, the application and the user. We propose an interaction framework for salient details from the perspective of the user, which dynamically updates the user and contextdependent definition of saliency based on relevance feedback. A number of instantiations of the framework are presented. 1.