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Fitting Parameterized ThreeDimensional Models to Images
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1991
"... Modelbased recognition and motion tracking depends upon the ability to solve for projection and model parameters that will best fit a 3D model to matching 2D image features. This paper extends current methods of parameter solving to handle objects with arbitrary curved surfaces and with any nu ..."
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Cited by 286 (8 self)
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Modelbased recognition and motion tracking depends upon the ability to solve for projection and model parameters that will best fit a 3D model to matching 2D image features. This paper extends current methods of parameter solving to handle objects with arbitrary curved surfaces and with any number of internal parameters representing articulations, variable dimensions, or surface deformations. Numerical
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 ...
Detection of Linear Features in SAR Images: Application to Road Network Extraction
, 1998
"... We propose a twostep algorithm for almost unsupervised detection of linear structures, in particular, main axes in road networks, as seen in synthetic aperture radar (SAR) images. The first step is local and is used to extract linear features from the speckle radar image, which are treated as roads ..."
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Cited by 62 (2 self)
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We propose a twostep algorithm for almost unsupervised detection of linear structures, in particular, main axes in road networks, as seen in synthetic aperture radar (SAR) images. The first step is local and is used to extract linear features from the speckle radar image, which are treated as roadsegment candidates. We present two local line detectors as well as a method for fusing information from these detectors. In the second global step, we identify the real roads among the segment candidates by defining a Markov random field (MRF) on a set of segments, which introduces contextual knowledge about the shape of road objects. The influence of the parameters on the road detection is studied and results are presented for various real radar images.
Invariant Geometric Evolutions of Surfaces and Volumetric Smoothing
, 1997
"... . The study of geometric flows for smoothing, multiscale representation, and analysis of two and threedimensional objects has received much attention in the past few years. In this paper, we first survey the geometric smoothing of curves and surfaces via geometric heattype flows, which are invari ..."
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Cited by 36 (11 self)
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. The study of geometric flows for smoothing, multiscale representation, and analysis of two and threedimensional objects has received much attention in the past few years. In this paper, we first survey the geometric smoothing of curves and surfaces via geometric heattype flows, which are invariant under the groups of Euclidean and affine motions. Second, using the general theory of differential invariants, we determine the general formula for a geometric hypersurface evolution which is invariant under a prescribed symmetry group. As an application, we present the simplest affine invariant flow for (convex) surfaces in threedimensional space, which, like the affineinvariant curve shortening flow, will be of fundamental importance in the processing of threedimensional images. Key words. invariant surface evolutions, partial differential equations, geometric smoothing, symmetry groups AMS subject classifications. 35K22, 53A15, 53A55, 53A20, 35B99 PII. S0036139994266311 1. Intro...
First order augmentations to tensor voting for boundary inference and multiscale analysis in 3d
 IEEE Trans. On Pattern Analysis and Machine Intelligence
, 2004
"... Abstract—Most computer vision applications require the reliable detection of boundaries. In the presence of outliers, missing data, orientation discontinuities, and occlusion, this problem is particularly challenging. We propose to address it by complementing the tensor voting framework, which was l ..."
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Cited by 22 (2 self)
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Abstract—Most computer vision applications require the reliable detection of boundaries. In the presence of outliers, missing data, orientation discontinuities, and occlusion, this problem is particularly challenging. We propose to address it by complementing the tensor voting framework, which was limited to second order properties, with first order representation and voting. First order voting fields and a mechanism to vote for 3D surface and volume boundaries and curve endpoints in 3D are defined. Boundary inference is also useful for a second difficult problem in grouping, namely, automatic scale selection. We propose an algorithm that automatically infers the smallest scale that can preserve the finest details. Our algorithm then proceeds with progressively larger scales to ensure continuity where it has not been achieved. Therefore, the proposed approach does not oversmooth features or delay the handling of boundaries and discontinuities until model misfit occurs. The interaction of smooth features, boundaries, and outliers is accommodated by the unified representation, making possible the perceptual organization of data in curves, surfaces, volumes, and their boundaries simultaneously. We present results on a variety of data sets to show the efficacy of the improved formalism. Index Terms—Tensor voting, first order voting, boundary inference, discontinuities, multiscale analysis, 3D perceptual organization. 1
Connected Components of Sets of Finite Perimeter and Applications to Image Processing
 JOURNAL OF THE EUROPEAN MATHEMATICAL SOCIETY
, 1999
"... This paper contains a systematic analysis of a natural measure theoretic notion of connectedness for sets of finite perimeter in R^N, introduced by H. Federer in the more general framework of the theory of currents. We provide a new and simpler proof of the existence and uniqueness of the decomposit ..."
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Cited by 21 (7 self)
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This paper contains a systematic analysis of a natural measure theoretic notion of connectedness for sets of finite perimeter in R^N, introduced by H. Federer in the more general framework of the theory of currents. We provide a new and simpler proof of the existence and uniqueness of the decomposition into the socalled Mconnected components. Moreover, we study carefully the structure of the essential boundary of these components and give in particular a reconstruction formula of a set of finite perimeter from the family of the boundaries of its components. In the two dimensional case we show that this notion of connectedness is comparable with the topological one, modulo the choice of a suitable representative in the equivalence class. Our strong motivation for this study is a mathematical justification of all those operations in image processing that involve connectedness and boundaries. As an application, we use this weak notion of connectedness to provide a rigorous mathemati...
Shape Representation and Recognition from Multiscale Curvature
, 1997
"... this paper, an approach is proposed for describing take into account the particular measurements to be made objects for the purposes of recognition. We deal with two from the smoothed data, specifically, the multiscale mea issues: building generalpurpose multiscale descriptions of surement of cur ..."
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Cited by 17 (0 self)
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this paper, an approach is proposed for describing take into account the particular measurements to be made objects for the purposes of recognition. We deal with two from the smoothed data, specifically, the multiscale mea issues: building generalpurpose multiscale descriptions of surement of curvature. The curvaturetuned smoothing curved objects, and extracting a concise set of attributes that can be used for recognition. Typical examples of the method we have developed and present here allows us to types of curve we are able to describe as both qualitatively obtain measurements which have not been subjected to similar, yet discriminably different, are shown in Fig. 1. an unnatural "flattening" or distortion as a result of the The method is based on the use of approximating curves smoothing [13]. Our technique is based on multiscale with simple curvature properties. In contrast, alternative smoothing with a specialized form of "regularization." We methods based on line segments or curvature extrema 1
Representing Planar Curves By Using A Scale Vector
 Pattern Recognition Letters
, 1994
"... This paper introduces a new approach to solving the problem of representing planar curves. We describe the 2D curve C not at all different scales oe, but each curve part C i of C, isolating a different structure at its single scale oe i . Therefore, we represent the planar curve at a scale vector ..."
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Cited by 14 (12 self)
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This paper introduces a new approach to solving the problem of representing planar curves. We describe the 2D curve C not at all different scales oe, but each curve part C i of C, isolating a different structure at its single scale oe i . Therefore, we represent the planar curve at a scale vector (oe 1 ; \Delta \Delta \Delta ; oe L ) supposing that the curve is partitioned in L parts C 1 ; \Delta \Delta \Delta ; CL . We propose an automatic method to divide the contour into the number of nonoverlappings parts C 1 ; \Delta \Delta \Delta ; CL , each of them showing a different underlying structure. This process requires neither the number of parts in the curve nor the minimum level of homogeneity for the entities within a particular part. The partition is based on three elements: a vector OE of statistical measures calculated to each class, a distance funtion d(OE i ; OE j ) between vectors corresponding to two different classes, and a halt criterion based on a measure of the improveme...
Boundary Simplification In Cartography Preserving The DifferentScale Shape Features
 Computers & Geosciences
, 1994
"... In this paper, we assume that cartographic boundaries have features at a variety of different degrees of simplification and therefore each line segment showing a different feature must be simplified at its proper degree. Our boundary simplification method preserves the characteristics of the shape f ..."
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Cited by 13 (11 self)
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In this paper, we assume that cartographic boundaries have features at a variety of different degrees of simplification and therefore each line segment showing a different feature must be simplified at its proper degree. Our boundary simplification method preserves the characteristics of the shape features, and therefore avoids missing fine features and overlooking coarse features. Here simplification consists of the dominant points detected on the line which has been previously segmented into a number of parts, each one showing a different feature at an appropiate degree of smoothing. We propose an automatic method to segment the line into a number of nonoverlapping parts, each one revealing a different feature. To find the best degree of simplification for each segment, we choose the simplification minimizing a normalized measure of the zeros of curvature of the segment. Key Words: Line simplification, segmentation, feature, degree of simplification, dominant points, Gaussian smoot...