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40
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 3903 (17 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.
Gaussian Networks for Direct Adaptive Control
 IEEE Transactions on Neural Networks
, 1992
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The variational approach to shape from shading
 Computer Vision, Graphics, and Image Processing
, 1986
"... We develop a systematic approach to the discovery of parallel iterative schemes for solving the shapefromshading problem on a grid. A standard procedure for finding such schemes is outlined, and subsequently used to derive several new ones. The shapefromshading problem is known to be mathematica ..."
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Cited by 141 (1 self)
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We develop a systematic approach to the discovery of parallel iterative schemes for solving the shapefromshading problem on a grid. A standard procedure for finding such schemes is outlined, and subsequently used to derive several new ones. The shapefromshading problem is known to be mathematically equivalent to a nonlinear firstorder partial differential equation in surface elevation. To avoid the problems inherent in methods used to solve such equations, we follow previous work in reformulating the problem as one of finding a surface orientation field that minimizes the integral of the brightness error. The calculus of variations is then employed to derive the appropriate Euler equations on which iterative schemes can be based. The problem of minimizing the integral of the brightness error term is ill posed, since it has an infinite number of solutions in terms of surface orientation fields. A previous method used a regularization technique to overcome this difficulty. An extra term was added to the integral to obtain an approximation to a solution that was as smooth as possible. We point out here that surface orientation has to obey an integrability constraint if it is to correspond to an underlying smooth surface. Regularization methods do not guarantee that the surface orientation recovered satisfies this constraint. see also "Shape from Shading" MIT Press.
Edge Detection Techniques  An Overview
 INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND IMAGE ANALYSIS
, 1998
"... In computer vision and image processing, edge detection concerns the localization of significant variations of the grey level image and the identification of the physical phenomena that originated them. This information is very useful for applications in 3D reconstruction, motion, recognition, image ..."
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Cited by 130 (2 self)
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In computer vision and image processing, edge detection concerns the localization of significant variations of the grey level image and the identification of the physical phenomena that originated them. This information is very useful for applications in 3D reconstruction, motion, recognition, image enhancement and restoration, image registration, image compression, and so on. Usually, edge detection requires smoothing and differentiation of the image. Differentiation is an illconditioned problem and smoothing results in a loss of information. It is difficult to design a general edge detection algorithm which performs well in many contexts and captures the requirements of subsequent processing stages. Consequently, over the history of digital image processing a variety of edge detectors have been devised which differ in their mathematical and algorithmic properties. This paper is an account of the current state of our understanding of edge detection. We propose an overview of research...
Specialization of perceptual processes
, 1993
"... In this report, I discuss the use of vision to support concrete, everyday activity. I will argue that a variety of interesting tasks can be solved using simple and inexpensive vision systems. I will provide a number of working examples in the form of a stateoftheart mobile robot, Polly, which us ..."
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Cited by 95 (6 self)
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In this report, I discuss the use of vision to support concrete, everyday activity. I will argue that a variety of interesting tasks can be solved using simple and inexpensive vision systems. I will provide a number of working examples in the form of a stateoftheart mobile robot, Polly, which uses vision to give primitive tours of the seventh oor of the MIT AI Laboratory. By current standards, the robot has a broad behavioral repertoire and is both simple and inexpensive (the complete robot was built for less than $20,000 using commercial boardlevel components). The approach I will use will be to treat the structure of the agent's activity its task and environmentas positive resources for the vision system designer. By performing a careful analysis of task and environment, the designer can determine a broad space of mechanisms which can perform the desired activity. My principal thesis is that for a broad range of activities, the space of applicable mechanisms will be broad enough to include a number mechanisms which are simple and economical. The simplest mechanisms that solve a given problem will typically be quite spe
Deformable contours: Modeling and extraction
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1995
"... This paper considers the problem of modeling and extracting arbitrary deformable contours from noisy images. We propose a global contour model based on a stable and regenerative shape matrix, which is invariant and unique under rigid motions. Combined with Markov random field to model local deformat ..."
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Cited by 88 (2 self)
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This paper considers the problem of modeling and extracting arbitrary deformable contours from noisy images. We propose a global contour model based on a stable and regenerative shape matrix, which is invariant and unique under rigid motions. Combined with Markov random field to model local deformations, this yields prior distribution that exerts influence over a global model while allowing for deformations. We then cast the problem of extraction into posterior estimation and show its equivalence to energy minimization of a generalized active contour model. We discuss pertinent issues in shape training, energy minimization, line search strategies, minimax regularization and initialization by generalized Hough transform. Finally, we present experimental results and compare its performance to rigid template matching.
Describing Surfaces
 Computer Vision, Graphics, and Image Processing
, 1985
"... This paper continues ou,' work' on vlsuM representations of threedimensional surfaces [Brady and Yuille 1984b]. The theoretical component o our work is a study of classes of surface curves as a source of constraint on the surface on which they lie, and as a basis for describing it. We ana ..."
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Cited by 62 (5 self)
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This paper continues ou,' work' on vlsuM representations of threedimensional surfaces [Brady and Yuille 1984b]. The theoretical component o our work is a study of classes of surface curves as a source of constraint on the surface on which they lie, and as a basis for describing it. We analyze bounding contours, sin face intersections, lines of cunature, and asymptotes. Our experimental work hives.igates whether the information suggested by our theoretical study can be computed reliably mid efficiently. We demonstrate algorithms that compute lines of curvature of a (Gaussian smoothed) surface; determine planar patches and umbi!ic regions; extract axes of surfaces of revolution and tube surfaces. We report preliminary results on adapting the curvature primM sketch algorithms of Asada and Brady [1984] to detect and describe surface intersections. () Massachusetts Institute of Technology, 1984 This report describes research done at the Artificial Intelligeice Laboratory of the Massachusetts Institute of Technology. Support for the ]aboratory's Artificial Intelligence reseat.oh is provided in par. by the Adwmced Research Projects Agency of the Department of Defense under Office of Naval Research contract N0001480C0505, the Office of Nax'al Research under contract number N000t477C0389, ,and the System Development Foundation. This wcrk was done while Haruo Asada was a visiting scientist at MIT on leave from Toshiba Corporation, Japan, and while Jean Ponce was a visking s.ientist on leave from I.'RIA, Paris, Fro,nee. ' Pr't of (t6:7)
Contour evolution, neighborhood deformation and local image flow: Curved surfaces in motion. Tech. rept. in preparation
, 1985
"... In the kinematic analysis of timevarying imagery, where the goal is to recover object surface structure and space motion from image flow, an appropriate representation for the flow field consists of a set of deformation parameters that describe the rate of change of an image neighborhood. In this p ..."
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Cited by 54 (1 self)
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In the kinematic analysis of timevarying imagery, where the goal is to recover object surface structure and space motion from image flow, an appropriate representation for the flow field consists of a set of deformation parameters that describe the rate of change of an image neighborhood. In this paper we develop methods for extracting these deformation parameters from evolving contours in an image sequence, the image contours being manifestations of surface texture seen in perspective projection. Our results follow directly from the analytic structure of the underlying image flow; no heuristics are imposed. The deformation parameters we seek are actually linear combinations of the Taylor series coefficients (through second derivatives) of the local image flow field. Thus, a byproduct of our approach is a secondorder polynomial approximation to the image flow in the neighborhood of a contour. For curved surfaces this approximation is only locally valid, but for planar surfaces it is globally valid (i.e., it is exact). Our analysis reveals an "aperture problem in the large " in which insufficient contour structure leaves the set of 12 deformation parameters underdetermined. We also assess the sensitivity of our method to the simulated effects of noise in the "normal flow " around contours as well as the angular field of view subtended by contours. The sensitivity analysis is carried out in the context of planar surfaces executing general rigidbody motions in space. Future work will address the additional considerations relevant to curved surface patches. 1.
Analog "Neuronal" Networks in Early Vision
, 1985
"... Many problems in early vision can be formulated in terms of minimizing an' energy or cost function. Examples are shapefromshading, edge detection, motion snatysis, structure from motion and surface interpolation (Poggio, Torre and Koch, 1985). It has been shown that all quadratic variational ..."
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Cited by 48 (11 self)
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Many problems in early vision can be formulated in terms of minimizing an' energy or cost function. Examples are shapefromshading, edge detection, motion snatysis, structure from motion and surface interpolation (Poggio, Torre and Koch, 1985). It has been shown that all quadratic variational problems, an important subset of early vision tasks, can be "solved" by linear, analog electrical or chemical networks (Poggio and Koch, 1985). In a variety of situations the cost function is nonquadratic, however, for instance in the presence of discontinuities. The use of nonquadratic cost functions raises the question of designing efficient algorithms for computing the optimal solution. Recently. Hopfield and Tank (1985) have shown that networks of nonlinear analog "neurons" can be effect. lye in computing the solution of optimization problems, In this paper, we show how these networks can be generalized to solve the nonconvex energy functionals of early vision. We illustrate this approach by implementing a specific network solving the problem of reconstructing a smooth surface while preserving its discontinuities from sparsely sampled data (Geman and Geman, 1984; Marroquin, 1984; Terzopoulos, 1984). These results suggest a novel computational strategy for solving such problems for both biological and artificial vision systems.
Energy Functions for Early Vision and Analog Networks.
 Biological Cybernetics
, 1987
"... This paper describes attempts to model the modules of early vision in terms of minimizing energy functions, in particular energy functions allowing discontinuities in the solution. It examines the success of using Hopfieldstyle analog networks for solving such problems. Finally it discusses the ..."
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Cited by 27 (2 self)
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This paper describes attempts to model the modules of early vision in terms of minimizing energy functions, in particular energy functions allowing discontinuities in the solution. It examines the success of using Hopfieldstyle analog networks for solving such problems. Finally it discusses the limitations of the energy function approach.