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98
A Model of Saliencybased Visual Attention for Rapid Scene Analysis
, 1998
"... A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented. Multiscale image features are combined into a single topographical saliency map. A dynamical neural network then selects attended locations in order of decreasing salie ..."
Abstract

Cited by 990 (56 self)
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A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented. Multiscale image features are combined into a single topographical saliency map. A dynamical neural network then selects attended locations in order of decreasing saliency. The system breaks down the complex problem of scene understanding by rapidly selecting, in a computationally efficient manner, conspicuous locations to be analyzed in detail. Index terms: Visual attention, scene analysis, feature extraction, target detection, visual search. \Pi I. Introduction Primates have a remarkable ability to interpret complex scenes in real time, despite the limited speed of the neuronal hardware available for such tasks. Intermediate and higher visual processes appear to select a subset of the available sensory information before further processing [1], most likely to reduce the complexity of scene analysis [2]. This selection appears to be implemented in the ...
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 85 (6 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 ...
The calculi of emergence: Computation, dynamics, and induction
 Physica D
, 1994
"... Defining structure and detecting the emergence of complexity in nature are inherently subjective, though essential, scientific activities. Despite the difficulties, these problems can be analyzed in terms of how modelbuilding observers infer from measurements the computational capabilities embedded ..."
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Cited by 79 (14 self)
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Defining structure and detecting the emergence of complexity in nature are inherently subjective, though essential, scientific activities. Despite the difficulties, these problems can be analyzed in terms of how modelbuilding observers infer from measurements the computational capabilities embedded in nonlinear processes. An observer’s notion of what is ordered, what is random, and what is complex in its environment depends directly on its computational resources: the amount of raw measurement data, of memory, and of time available for estimation and inference. The discovery of structure in an environment depends more critically and subtlely, though, on how those resources are organized. The descriptive power of the observer’s chosen (or implicit) computational model class, for example, can be an overwhelming determinant in finding regularity in data. This paper presents an overview of an inductive framework — hierarchicalmachine reconstruction — in which the emergence of complexity is associated with the innovation of new computational model classes. Complexity metrics for detecting structure and quantifying emergence, along with an analysis of the constraints on the dynamics of innovation, are outlined. Illustrative examples are drawn from the onset of unpredictability in nonlinear systems, finitary nondeterministic processes, and
Globally optimal regions and boundaries as minimum ratio weight cycles
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2001
"... Abstract. 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,.. ..."
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Cited by 72 (2 self)
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Abstract. 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. 1.
Beamlets and Multiscale Image Analysis
 in Multiscale and Multiresolution Methods
, 2001
"... We describe a framework for multiscale image analysis in which line segments play a role analogous to the role played by points in wavelet analysis. ..."
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Cited by 55 (16 self)
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We describe a framework for multiscale image analysis in which line segments play a role analogous to the role played by points in wavelet analysis.
PreAttentive Segmentation in the Primary Visual Cortex
, 2000
"... The activities of neurons in primary visual cortex have been shown to be significantly influenced by stimuli outside their classical receptive fields. We propose that these contextual influences serve preattentive visual segmentation by causing relatively higher neural responses to important or con ..."
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Cited by 51 (0 self)
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The activities of neurons in primary visual cortex have been shown to be significantly influenced by stimuli outside their classical receptive fields. We propose that these contextual influences serve preattentive visual segmentation by causing relatively higher neural responses to important or conspicuous image locations, making them more salient for perceptual popout. These locations include boundaries between regions, smooth contours, and popout targets against backgrounds. The mark of these locations is the breakdown of spatial homogeneity in the input, for instance, at the border between two texture regions of equal mean luminance. This breakdown causes changes in contextual influences, often resulting in higher responses at the border than at surrounding locations. This proposal is implemented in a biologically based model of V1 in which contextual influences are mediated by intracortical horizontal connections. The behavior of the model is demonstrated using examples of text...
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 38 (9 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.
Is Anything Ever New? Considering Emergence
 IN
, 1994
"... This brief essay reviews an approach to defining and then detecting the emergence of complexity in nonlinear processes. It is, in fact, a synopsis of Reference [1] that leaves out the technical details in an attempt to clarify the motivations behind the approach. The central puzzle ..."
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Cited by 35 (4 self)
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This brief essay reviews an approach to defining and then detecting the emergence of complexity in nonlinear processes. It is, in fact, a synopsis of Reference [1] that leaves out the technical details in an attempt to clarify the motivations behind the approach. The central puzzle
Globally Optimal Regions and Boundaries
, 1999
"... We propose a new form of energy functional for the segmentation of regions in images, and an efficient method for finding its global optima. The energy can have contributions from both the region and its boundary, thus combining the best features of region and boundarybased approaches to segmentat ..."
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Cited by 33 (2 self)
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We propose a new form of energy functional for the segmentation of regions in images, and an efficient method for finding its global optima. The energy can have contributions from both the region and its boundary, thus combining the best features of region and boundarybased approaches to segmentation. By transforming the region energy into a boundary energy, we can treat both contributions on an equal footing, and solve the global optimization problem as a minimum mean weight cycle problem on a directed graph. The simple, polynomialtime algorithm requires no initialization and is highly parallelizable.