Results 1  10
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37
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 60 (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 55 (11 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 48 (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.
Signature detection and matching for document image retrieval
 IEEE Trans. Pattern Anal. Mach. Intell
, 2009
"... Abstract—As one of the most pervasive methods of individual identification and document authentication, signatures present convincing evidence and provide an important form of indexing for effective document image processing and retrieval in a broad range of applications. However, detection and segm ..."
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Cited by 15 (4 self)
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Abstract—As one of the most pervasive methods of individual identification and document authentication, signatures present convincing evidence and provide an important form of indexing for effective document image processing and retrieval in a broad range of applications. However, detection and segmentation of freeform objects such as signatures from clustered background is currently an open document analysis problem. In this paper, we focus on two fundamental problems in signaturebased document image retrieval. First, we propose a novel multiscale approach to jointly detecting and segmenting signatures from document images. Rather than focusing on local features that typically have large variations, our approach captures the structural saliency using a signature production model and computes the dynamic curvature of 2D contour fragments over multiple scales. This detection framework is general and computationally tractable. Second, we treat the problem of signature retrieval in the unconstrained setting of translation, scale, and rotation invariant nonrigid shape matching. We propose two novel measures of shape dissimilarity based on anisotropic scaling and registration residual error and present a supervised learning framework for combining complementary shape information from different dissimilarity metrics using LDA. We quantitatively study stateoftheart shape representations, shape matching algorithms, measures of dissimilarity, and the use of multiple instances as query in document image retrieval. We further demonstrate our matching techniques in offline signature verification. Extensive experiments using large realworld collections of English and Arabic machineprinted and handwritten documents demonstrate the excellent performance of our approaches. Index Terms—Document image analysis and retrieval, signature detection and segmentation, signature matching, structural saliency, deformable shape, measure of shape dissimilarity. Ç 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.
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 9 (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.
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 9 (6 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.
Object of Interest Detection by Saliency Learning
 ECCV 2010, Part II. LNCS
, 2010
"... Abstract. In this paper, we present a method for object of interest detection. This method is statistical in nature and hinges in a model which combines salient features using a mixture of linear support vector machines. It exploits a divideandconquer strategy by partitioning the feature space int ..."
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Cited by 9 (0 self)
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Abstract. In this paper, we present a method for object of interest detection. This method is statistical in nature and hinges in a model which combines salient features using a mixture of linear support vector machines. It exploits a divideandconquer strategy by partitioning the feature space into subregions of linearly separable datapoints. This yields a structured learning approach where we learn a linear support vector machine for each region, the mixture weights, and the combination parameters for each of the salient features at hand. Thus, the method learns the combination of salient features such that a mixture of classifiers can be used to recover objects of interest in the image. We illustrate the utility of the method by applying our algorithm to the MSRA Salient Object Database. 1
On the distribution of saliency
 In CVPR, 2004. [Cha31] C.V.L Charlier. Applications
"... Abstract. The calculation of salient structures is one of the early and basic ideas of perceptual organization in Computer Vision. Saliency algorithms typically mark edgepoints with some saliency measure, growing with the length and smoothness of the curve on which this edgepoint lies. We propose ..."
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Cited by 8 (0 self)
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Abstract. The calculation of salient structures is one of the early and basic ideas of perceptual organization in Computer Vision. Saliency algorithms typically mark edgepoints with some saliency measure, growing with the length and smoothness of the curve on which this edgepoint lies. We propose here a modified saliency estimation mechanism, which is based on probabilistically specified grouping cues and on curve length distributions. In the context of the proposed method, the Shaashua and Ullman saliency mechanism [SU88] may be interpreted as a process trying to detect the curve with maximal expected length. The proposed approach lends itself to different types of generalizations, and in particular, to saliencies based on different cues, in a systematic rigorous way. To demonstrate this, we created a saliency process that is based on grey level similarity. The proposed saliency allows, in principle, to specify many saliency functions depending on the length distribution. We show, however, that only a limited class of saliency functions may be rigorously optimized by a local process. Following this negative result we focus on probabilistic analysis of expected length saliencies. Using ergodicity and asymptotic analysis, we derive the saliency distribution associated with the main curves and with the rest of the image. We then extend this analysis to finitelength curves. Based on the derived distributions, we show how to set a threshold on the saliency for deciding optimally between figure and background (we provide an approximate explicit expression), how to choose cues that are usable for saliency, and how to estimate bounds on the saliency performance.
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 6 (1 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.