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An O(N) Iterative Solution to the Poisson Equation in Lowlevel Vision Problems
 In IEEE conference on Computer Vision & Pattern Recognition
, 1994
"... this paper, we present a novel iterative numerical solution to the Poisson equation whose solution is needed in a variety of lowlevel vision problems. Our algorithm is an O(N) (N being the number of discretization points) iterative technique and does not make any assumptions on the shape of the inp ..."
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Cited by 10 (4 self)
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this paper, we present a novel iterative numerical solution to the Poisson equation whose solution is needed in a variety of lowlevel vision problems. Our algorithm is an O(N) (N being the number of discretization points) iterative technique and does not make any assumptions on the shape
19 Green's Functions of Matching Equations: A Unifying Approach for Lowlevel Vision Problems
"... ..."
Learning lowlevel vision
 International Journal of Computer Vision
, 2000
"... We show a learningbased method for lowlevel vision problems. We setup a Markov network of patches of the image and the underlying scene. A factorization approximation allows us to easily learn the parameters of the Markov network from synthetic examples of image/scene pairs, and to e ciently prop ..."
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Cited by 579 (30 self)
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We show a learningbased method for lowlevel vision problems. We setup a Markov network of patches of the image and the underlying scene. A factorization approximation allows us to easily learn the parameters of the Markov network from synthetic examples of image/scene pairs, and to e ciently
Markov Networks For LowLevel Vision
 In Workshop on Statistical and Computational Theories of Vision
, 1999
"... We seek a learningbased algorithm that applies to various lowlevel vision problems. For each problem, wewant to #nd the scene interpretation that best explains image data. For example, wemaywant to infer the projected velocities #scene# which best explain two consecutive image frames #image#. F ..."
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Cited by 5 (1 self)
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We seek a learningbased algorithm that applies to various lowlevel vision problems. For each problem, wewant to #nd the scene interpretation that best explains image data. For example, wemaywant to infer the projected velocities #scene# which best explain two consecutive image frames #image
Learning LowLevel Vision
, 2000
"... We describe a learningbased method for lowlevel vision problems—estimating scenes from images. We generate a synthetic world of scenes and their corresponding rendered images, modeling their relationships with a Markov network. Bayesian belief propagation allows us to efficiently find a local max ..."
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We describe a learningbased method for lowlevel vision problems—estimating scenes from images. We generate a synthetic world of scenes and their corresponding rendered images, modeling their relationships with a Markov network. Bayesian belief propagation allows us to efficiently find a local
Unifying LowLevel Vision
, 2011
"... This white paper supports the goal of establishing Computer Vision as a coherent intellectual discipline1 by suggesting a specific agenda for the unification of many lowlevel vision principles, algorithms, and data structures. Our goal is to identify a set of highly related lowlevel vision problem ..."
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problems, define their common structure, and establish a coherent intellectual discipline around the shared structures. We believe this can unify and simplify the understanding, teaching, and quality of lowlevel computer vision as a science. There is a set of lowlevel vision tasks that are closely
Markov Random Field Models in Computer Vision
, 1994
"... . A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is defined as the maximum a posteriori (MAP) probability estimate of the true labeling. The posterior probability is usually derived from a prior model and a likelihood model. The l ..."
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Cited by 516 (18 self)
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in low and high level computer vision. The unification is made possible due to a recent advance in MRF modeling for high level object recognition. Such unification provides a systematic approach for vision modeling based on sound mathematical principles. 1 Introduction Since its beginning in early 1960
Computer Vision
, 1982
"... Driver inattention is one of the main causes of traffic accidents. Monitoring a driver to detect inattention is a complex problem that involves physiological and behavioral elements. Different approaches have been made, and among them Computer Vision has the potential of monitoring the person behind ..."
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Cited by 1041 (11 self)
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Driver inattention is one of the main causes of traffic accidents. Monitoring a driver to detect inattention is a complex problem that involves physiological and behavioral elements. Different approaches have been made, and among them Computer Vision has the potential of monitoring the person
An Experimental Comparison of MinCut/MaxFlow Algorithms for Energy Minimization in Vision
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2001
"... After [10, 15, 12, 2, 4] minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in lowlevel vision. The combinatorial optimization literature provides many mincut/maxflow algorithms with different polynomial time compl ..."
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Cited by 1315 (53 self)
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After [10, 15, 12, 2, 4] minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in lowlevel vision. The combinatorial optimization literature provides many mincut/maxflow algorithms with different polynomial time
ScaleSpace Theory in Computer Vision
, 1994
"... A basic problem when deriving information from measured data, such as images, originates from the fact that objects in the world, and hence image structures, exist as meaningful entities only over certain ranges of scale. "ScaleSpace Theory in Computer Vision" describes a formal theory fo ..."
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Cited by 625 (21 self)
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A basic problem when deriving information from measured data, such as images, originates from the fact that objects in the world, and hence image structures, exist as meaningful entities only over certain ranges of scale. "ScaleSpace Theory in Computer Vision" describes a formal theory
Results 1  10
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24,517