Results 1  10
of
25
On Edge Detection
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1984
"... Edge detection is the process that attempts to characterize the intensity changes in the image in terms of the physical processes that have originated them. A critical, intermediate goal of edge detection is the detection and characterization of significant intensity changes. This paper discusses th ..."
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Cited by 243 (7 self)
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Edge detection is the process that attempts to characterize the intensity changes in the image in terms of the physical processes that have originated them. A critical, intermediate goal of edge detection is the detection and characterization of significant intensity changes. This paper discusses this part of the edge d6tection problem. To characterize the types of intensity changes derivatives of different types, and possibly different scales, are needed. Thus, we consider this part of edge detection as a problem in numerical differentiation.
ModelBased Recognition in Robot Vision
 ACM Computing Surveys
, 1986
"... This paper presents a comparative study and survey of modelbased objectrecognition algorithms for robot vision. The goal of these algorithms is to recognize the identity, position, and orientation of randomly oriented industrial parts. In one form this is commonly referred to as the “binpicking ” ..."
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Cited by 195 (0 self)
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This paper presents a comparative study and survey of modelbased objectrecognition algorithms for robot vision. The goal of these algorithms is to recognize the identity, position, and orientation of randomly oriented industrial parts. In one form this is commonly referred to as the “binpicking ” problem, in which the parts to be recognized are presented in a jumbled bin. The paper is organized according to 2D, 2&D, and 3D object representations, which are used as the basis for the recognition algorithms. Three
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)
An Extremum Principle for Shape from Contour
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1983
"... An extremum principle is developed that determines threedimensional surface orientation from a twodimensional contour. The principle maximizes the ratio of the area to the square of the perimeter, a measure of the compactness or symmetry of the threedimensional surface. I;he principle interpre ..."
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Cited by 49 (1 self)
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An extremum principle is developed that determines threedimensional surface orientation from a twodimensional contour. The principle maximizes the ratio of the area to the square of the perimeter, a measure of the compactness or symmetry of the threedimensional surface. I;he principle interprets regular figures correctly and it interprets skew symmetries as oriented real symmetries. The maximum likelihood method approximates the principle on irregular figures, but we show that it consistently overestimates the slant of an ellipse.
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.
Tracking and describing deformable objects using active contour models. Mcrcim technical report cim909
, 1990
"... This version is a reprint produced in February 2003, while at Brown University. It contains a few updates and corrections. ..."
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Cited by 12 (2 self)
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This version is a reprint produced in February 2003, while at Brown University. It contains a few updates and corrections.
Picking Up an Object From a Pile of Objects
 Proceedings of the First International Symposium on Robotics Research
, 1983
"... This paper describes a handeye system we developed to perform the binpicking task. Two basic tools are employed: the photometric stereo method and the extended Gaussian image. The photometric stereo method generates the surface normal distribution of a scene. The extended Gaussian image allows us t ..."
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Cited by 11 (3 self)
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This paper describes a handeye system we developed to perform the binpicking task. Two basic tools are employed: the photometric stereo method and the extended Gaussian image. The photometric stereo method generates the surface normal distribution of a scene. The extended Gaussian image allows us to determine the attitude of the object based on the normal distribution.
Error analysis of 3D shape construction from structured lighting
 Pattern Recognition
, 1996
"... Abstract In this paper, we present a detailed model and analysis of several error sources and thier effects on measuring threedimensional (3D) surface properties using the structured lighting technique. The analysis is based on a general system configuration and identifies three types of error surc ..."
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Cited by 8 (1 self)
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Abstract In this paper, we present a detailed model and analysis of several error sources and thier effects on measuring threedimensional (3D) surface properties using the structured lighting technique. The analysis is based on a general system configuration and identifies three types of error surcessystem modeling error, image processing error and experimental error. Absolute and relative error bounds in obtaining 3D surface orientation and curvature measurements using structured lighting are derived in terms of the system parameters and likely error sources. In addition to the quantization error, other likely error sources in system modeling and experimental setup are also considered. Even though our analysis is on structured lighting, the results are readily applicable to other triangulationbased techniques such as stereopsis. Finally, our analysis focuses on error in inferring surface orientation and principal surface curvature. Such analyses, to our knowledge, have never been attempted before. Image processing Structured light Orientation Curvature Error analysis 1.
Design and Evaluation of Feature Detectors
, 1998
"... Many applications in both image processing and computational vision rely upon the robust detection of parametric image features and the accurate estimation of their parameters. In this thesis, I address three fundamental questions related to the design and evaluation of parametric feature detectors. ..."
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Cited by 7 (0 self)
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Many applications in both image processing and computational vision rely upon the robust detection of parametric image features and the accurate estimation of their parameters. In this thesis, I address three fundamental questions related to the design and evaluation of parametric feature detectors. Most feature detectors have been designed to detect a single type of feature, more often than not, the step edge. A large number of other features are also of interest. Since the task of designing a feature detector is very time consuming, repeating the design effort for each feature is wasteful. To address this deficiency, in the first part of this thesis I develop an algorithm that takes as input a description of a parametric feature and automatically constructs a detector for it. The development of many feature detectors begins with an ideal model of the feature. Since image data are noisy, feature detectors must actually detect features that are almost, but not quite, ideal. Many exist...