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Snakes: Active contour models
 INTERNATIONAL JOURNAL OF COMPUTER VISION
, 1988
"... A snake is an energyminimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines and edges. Snakes are active contour models: they lock onto nearby edges, localizing them accurately. Scalespace continuation can be used to enlarge ..."
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Cited by 3084 (16 self)
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A snake is an energyminimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines and edges. Snakes are active contour models: they lock onto nearby edges, localizing them accurately. Scalespace continuation can be used to enlarge the capture region surrounding a feature. Snakes provide a unified account of a number of visual problems, including detection of edges, lines, and subjective contours; motion tracking; and stereo matching. We have used snakes successfully for interactive interpretation, in which userimposed constraint forces guide the snake near features of interest.
Trace inference, curvature consistency, and curve detection
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1989
"... We describe a novel approach to curve inference based on curvature information. The inference procedure is divided into two stages: a trace inference stage, to which this paper is devoted, and a curve synthesis stage, which will be treated in a separate paper. It is shown that recovery of the trace ..."
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Cited by 201 (13 self)
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We describe a novel approach to curve inference based on curvature information. The inference procedure is divided into two stages: a trace inference stage, to which this paper is devoted, and a curve synthesis stage, which will be treated in a separate paper. It is shown that recovery of the trace of a curve requires estimating local models for the curve at the same time, and that tangent and curvature information are sufficient. These make it possible to specify powerful constraints between estimated tangents to a curve, in terms of a neighborhood relationship called cocircularity and between curvature estimates, in terms of a curvature consistency relation. Because all curve information is quantized, special care must be taken to obtain accurate estimates of trace points, tangents and curvatures. This issue is addressed specifically by the introduction of a smoothness constraint and a maximum curvature constraint. The procedure is applied to two types of images, artificial images designed to evaluate curvature and noise sensitivity, and natural images.
Frameworks for Cooperation in Distributed Problem Solving
 IEEE Transactions on Systems, Man, and Cybernetics
, 1981
"... Abstract — Two forms of cooperation in distributed problem solving are considered: tasksharing and resultsharing. In the former, nodes assist each other by sharing the computational load for the execution of subtasks of the overall problem. In the latter, nodes assist each other by sharing partial ..."
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Cited by 165 (1 self)
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Abstract — Two forms of cooperation in distributed problem solving are considered: tasksharing and resultsharing. In the former, nodes assist each other by sharing the computational load for the execution of subtasks of the overall problem. In the latter, nodes assist each other by sharing partial results which are based on somewhat different perspectives on the overall problem. Different perspectives arise because the nodes use different knowledge sources (KS’s) (e.g., syntax versus acoustics in the case of a speechunderstanding system) or different data (e.g., data that is sensed at different locations in the case of a distributed sensing system). Particular attention is given to control and to internode communication for the two forms of cooperation. For each, the basic methodology is presented and systems in which it has been used are described. The two forms are then compared and the types of applications for which they are suitable are considered. I. DISTRIBUTED PROBLEM SOLVING
Algorithms for the Satisfiability (SAT) Problem: A Survey
 DIMACS Series in Discrete Mathematics and Theoretical Computer Science
, 1996
"... . The satisfiability (SAT) problem is a core problem in mathematical logic and computing theory. In practice, SAT is fundamental in solving many problems in automated reasoning, computeraided design, computeraided manufacturing, machine vision, database, robotics, integrated circuit design, compute ..."
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Cited by 127 (3 self)
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. The satisfiability (SAT) problem is a core problem in mathematical logic and computing theory. In practice, SAT is fundamental in solving many problems in automated reasoning, computeraided design, computeraided manufacturing, machine vision, database, robotics, integrated circuit design, computer architecture design, and computer network design. Traditional methods treat SAT as a discrete, constrained decision problem. In recent years, many optimization methods, parallel algorithms, and practical techniques have been developed for solving SAT. In this survey, we present a general framework (an algorithm space) that integrates existing SAT algorithms into a unified perspective. We describe sequential and parallel SAT algorithms including variable splitting, resolution, local search, global optimization, mathematical programming, and practical SAT algorithms. We give performance evaluation of some existing SAT algorithms. Finally, we provide a set of practical applications of the sat...
Functionally accurate, cooperative distributed systems
 IEEE Transactions on Systems, Man, and Cybernetics
, 1981
"... A new approach for structuring distributed processing systems, called functionally accurate, cooperative (FA/C), is proposed. The approach differs from conventional ones in its emphasis on handling distributioncaused uncertainty and errors as an integral part of the network problemsolving process. ..."
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Cited by 92 (18 self)
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A new approach for structuring distributed processing systems, called functionally accurate, cooperative (FA/C), is proposed. The approach differs from conventional ones in its emphasis on handling distributioncaused uncertainty and errors as an integral part of the network problemsolving process. In this approach nodes cooperatively problem solve by exchanging partial tentative results (at various levels of abstraction) within the context of common goals. The approach is especially suited to applications in which the data necessary to achieve a solution cannot be partitioned in such a way that a node can complete a task without seeing the intermediate state of task processing at other nodes. Much of the inspiration for the FA/C approach comes from the mechanisms used in knowledgebased artificial intelligence (AI) systems for resolving uncertainty caused by noisy input data and the use of approximate knowledge. The appropriateness of the FA/C approach is explored in three application domains: distributed interpretation, distributed network trafficlight control, and distributed planning. Additionally, the relationship between the approach and the structure of management organizations is developed. Finally, a number of current research directions necessary to more fully develop the FA/C approach are outlined. These research directions include distributed search, the integration of implicit and explicit forms of control, and distributed planning and organizational selfdesign. I.
From Surfaces to Objects: Computer Vision and ThreeDimensional Scene Analysis
, 1989
"... This book was originally published by John Wiley and Sons, ..."
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Cited by 29 (10 self)
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This book was originally published by John Wiley and Sons,
Edges: Saliency measures and automatic thresholding
 MACHINE VISION AND APPLICATION
, 1997
"... Edges are useful features for structural image analysis, but the output of standard edge detectors must be thresholded to remove the many spurious edges. This paper describes experiments with both new and old techniques for: 1) automatically determining appropriate edge threshold values, and 2) det ..."
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Cited by 23 (5 self)
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Edges are useful features for structural image analysis, but the output of standard edge detectors must be thresholded to remove the many spurious edges. This paper describes experiments with both new and old techniques for: 1) automatically determining appropriate edge threshold values, and 2) determining edge saliency (as alternatives to gradient magnitude).
The leastdisturbance principle and weak constraints
, 1983
"... Certain problems, notably in computer vision, involve adjusting a set of realvalued labels to satisfy certain constraints. They can be formulated as optimisation problems, using the 'leastdisturbance' principle: the minimal alteration is made to the labels that will achieve a consistent labelling. ..."
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Cited by 15 (1 self)
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Certain problems, notably in computer vision, involve adjusting a set of realvalued labels to satisfy certain constraints. They can be formulated as optimisation problems, using the 'leastdisturbance' principle: the minimal alteration is made to the labels that will achieve a consistent labelling. Under certain linear constraints, the solution can be achieved iteratively and in parallel, by hillclimbing. However, where 'weak' constraints are imposed on the labels constraints that may be broken at a cost the optimisation problem becomes nonconvex; a continuous search for the solution is no longer satisfactory. A strategy is proposed for this case, by construction of convex envelopes and by the use of 'graduated' nonconvexity.
An Overview of DAI: Viewing Distributed AI as Distributed Search
 Journal of Japanese Society for Artificial Intelligence
, 1990
"... this paper. Each of my colleagues has contributed important insights, beginning with my first published article on DAI with Lee Erman, through the longterm joint research efforts with Daniel Corkill and Edmund Durfee, and continuing in my recent work with Susan Conry, Robert Meyer, Kazuhiro Kuwabar ..."
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Cited by 14 (0 self)
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this paper. Each of my colleagues has contributed important insights, beginning with my first published article on DAI with Lee Erman, through the longterm joint research efforts with Daniel Corkill and Edmund Durfee, and continuing in my recent work with Susan Conry, Robert Meyer, Kazuhiro Kuwabara, Keith Decker, Susan Lander, Brigitte Maitre, and Hassan Laasri. References
The Curve Indicator Random Field: Curve Organization Via Edge Correlation
 In Perceptual Organization for Artificial Vision Systems
, 2000
"... Can the organization of local edge measurements into curves be directly related to natural image structure? By viewing curve organization as a statistical estimation problem, we suggest that it can. In particular, the classical Gestalt perceptual organization cues of proximity and good continuation ..."
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Cited by 11 (1 self)
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Can the organization of local edge measurements into curves be directly related to natural image structure? By viewing curve organization as a statistical estimation problem, we suggest that it can. In particular, the classical Gestalt perceptual organization cues of proximity and good continuationthe basis of many current curve organization systemscan be statistically measured in images. As a prior for our estimation approach we introduce the curve indicator random field. In contrast to other techniques that require contour closure or are based on a sparse set of detected edges, the curve indicator random field emphasizes the shortdistance, dense nature of organizing curve elements into (possibly) open curves. Its explicit formulation allows the calculation of its properties such as its autocorrelation. On the one hand, the curve indicator random field leads us to introduce the oriented Wiener filter, capturing the blur and noise inherent in the edge measurement process. On the other, it suggests we seek such correlations in natural images. We present the results of some initial edge correlation measurements that not only confirm the presence of Gestalt cues, but also suggest that curvature has a role in curve organization.