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53
MLESAC: A New Robust Estimator with Application to Estimating Image Geometry
 Computer Vision and Image Understanding
, 2000
"... A new method is presented for robustly estimating multiple view relations from point correspondences. The method comprises two parts. The first is a new robust estimator MLESAC which is a generalization of the RANSAC estimator. It adopts the same sampling strategy as RANSAC to generate putative solu ..."
Abstract

Cited by 241 (8 self)
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A new method is presented for robustly estimating multiple view relations from point correspondences. The method comprises two parts. The first is a new robust estimator MLESAC which is a generalization of the RANSAC estimator. It adopts the same sampling strategy as RANSAC to generate putative solutions, but chooses the solution that maximizes the likelihood rather than just the number of inliers. The second part of the algorithm is a general purpose method for automatically parameterizing these relations, using the output of MLESAC. A difficulty with multiview image relations is that there are often nonlinear constraints between the parameters, making optimization a difficult task. The parameterization method overcomes the difficulty of nonlinear constraints and conducts a constrained optimization. The method is general and its use is illustrated for the estimation of fundamental matrices, image–image homographies, and quadratic transformations. Results are given for both synthetic and real images. It is demonstrated that the method gives results equal or superior to those of previous approaches. c ○ 2000 Academic Press 1.
A Factorization Based Algorithm for MultiImage Projective Structure and Motion
, 1996
"... . We propose a method for the recovery of projective shape and motion from multiple images of a scene by the factorization of a matrix containing the images of all points in all views. This factorization is only possible when the image points are correctly scaled. The major technical contribution of ..."
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Cited by 212 (15 self)
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. We propose a method for the recovery of projective shape and motion from multiple images of a scene by the factorization of a matrix containing the images of all points in all views. This factorization is only possible when the image points are correctly scaled. The major technical contribution of this paper is a practical method for the recovery of these scalings, using only fundamental matrices and epipoles estimated from the image data. The resulting projective reconstruction algorithm runs quickly and provides accurate reconstructions. Results are presented for simulated and real images. 1 Introduction In the last few years, the geometric and algebraic relations between uncalibrated views have found lively interest in the computer vision community. A first key result states that, from two uncalibrated views, one can recover the 3D structure of a scene up to an unknown projective transformation [Fau92, HGC92]. The information one needs to do so is entirely contained in the fundam...
Autocalibration and the absolute quadric
 in Proc. IEEE Conf. Computer Vision, Pattern Recognition
, 1997
"... We describe a new method for camera autocalibration and scaled Euclidean structure and motion, from three or more views taken by a moving camera with fixed but unknown intrinsic parameters. The motion constancy of these is used to rectify an initial projective reconstruction. Euclidean scene structu ..."
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Cited by 210 (7 self)
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We describe a new method for camera autocalibration and scaled Euclidean structure and motion, from three or more views taken by a moving camera with fixed but unknown intrinsic parameters. The motion constancy of these is used to rectify an initial projective reconstruction. Euclidean scene structure is formulated in terms of the absolute quadric — the singular dual 3D quadric ( rank 3 matrix) giving the Euclidean dotproduct between plane normals. This is equivalent to the traditional absolute conic but simpler to use. It encodes both affine and Euclidean structure, and projects very simply to the dual absolute image conic which encodes camera calibration. Requiring the projection to be constant gives a bilinear constraint between the absolute quadric and image conic, from which both can be recovered nonlinearly from images, or quasilinearly from. Calibration and Euclidean structure follow easily. The nonlinear method is stabler, faster, more accurate and more general than the quasilinear one. It is based on a general constrained optimization technique — sequential quadratic programming — that may well be useful in other vision problems.
On the geometry and algebra of the point and line correspondences between N images
, 1995
"... We explore the geometric and algebraic relations that exist between correspondences of points and lines in an arbitrary number of images. We propose to use the formalism of the GrassmannCayley algebra as the simplest way to make both geometric and algebraic statements in a very synthetic and effect ..."
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Cited by 149 (6 self)
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We explore the geometric and algebraic relations that exist between correspondences of points and lines in an arbitrary number of images. We propose to use the formalism of the GrassmannCayley algebra as the simplest way to make both geometric and algebraic statements in a very synthetic and effective way (i.e. allowing actual computation if needed). We have a fairly complete picture of the situation in the case of points: there are only three types of algebraic relations which are satisfied by the coordinates of the images of a 3D point: bilinear relations arising when we consider pairs of images among the N and which are the wellknown epipolar constraints, trilinear relations arising when we consider triples of images among the N , and quadrilinear relations arising when we consider fourtuples of images among the N . In the case of lines, we show how the traditional perspective projection equation can be suitably generalized and that in the case of three images there exist two in...
Factorization methods for projective structure and motion
 In IEEE Conf. Computer Vision & Pattern Recognition
, 1996
"... This paper describes a family of factorizationbased algorithms that recover 3D projective structure and motion from multiple uncalibrated perspective images of 3D points and lines. They can be viewed as generalizations of the TomasiKanade algorithm from affine to fully perspective cameras, and fro ..."
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Cited by 106 (5 self)
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This paper describes a family of factorizationbased algorithms that recover 3D projective structure and motion from multiple uncalibrated perspective images of 3D points and lines. They can be viewed as generalizations of the TomasiKanade algorithm from affine to fully perspective cameras, and from points to lines. They make no restrictive assumptions about scene or camera geometry, and unlike most existing reconstruction methods they do not rely on ‘privileged’ points or images. All of the available image data is used, and each feature in each image is treated uniformly. The key to projective factorization is the recovery of a consistent set of projective depths (scale factors) for the image points: this is done using fundamental matrices and epipoles estimated from the image data. We compare the performance of the new techniques with several existing ones, and also describe an approximate factorization method that gives similar results to SVDbased factorization, but runs much more quickly for large problems.
Robust Parameterization and Computation of the Trifocal Tensor
 Image and Vision Computing
, 1997
"... The constraint that rigid motion places on the image positions of points and lines over three views is captured by the trifocal tensor. This paper demonstrates a novel robust estimator of the trifocal tensor, based on a minimum number of correspondences across an image triplet. In addition, it i ..."
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Cited by 98 (22 self)
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The constraint that rigid motion places on the image positions of points and lines over three views is captured by the trifocal tensor. This paper demonstrates a novel robust estimator of the trifocal tensor, based on a minimum number of correspondences across an image triplet. In addition, it is shown how the robust estimate can be used to find a minimal parameterization that enforces the constraints between the elements of the tensor. The matching techniques used to estimate the tensor are both robust (detecting and discarding mismatches) and fully automatic. Results are given for real image sequences. 1 Introduction The trifocal tensor plays a similar role for three views to that played by the fundamental matrix for two. It encapsulates all the (projective) geometric constraints between three views that are independent of scene structure. The tensor only depends on the motion between views and the internal parameters of the cameras, but it can be computed from image corre...
Lines and Point in Three Views and the Trifocal Tensor
, 1997
"... This paper disc#274# the basic role of the trifoc al tensor insc#37 rec# nstr uc#r# n from three views. This 3 3 tensor plays a role in the analysis of sc#422 from three views analogous to the role played by the fundamental matrix in the twoviewc ase. In partic ular, the trifoc al tensor may ..."
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Cited by 74 (3 self)
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This paper disc#274# the basic role of the trifoc al tensor insc#37 rec# nstr uc#r# n from three views. This 3 3 tensor plays a role in the analysis of sc#422 from three views analogous to the role played by the fundamental matrix in the twoviewc ase. In partic ular, the trifoc al tensor may bec omputed by a linear algorithm from a set of 13 linec orrespondenc#3 in three views. It is further shown in this paper, that the trifoc al tensor is essentially identic## to a set ofc oe#c#99 ts introduc#5 by Shashua toe#ec# point transfer in the three viewc##22 This observation means that the 13line algorithm may be extended to allow for thec omputation of the trifoc al tensor given any mixture of su#c#36 tly many line and pointc orrespondenc#9# From the trifoc al tensor thec amera matric## of the images may be c#25371# and the sc#35 may berec#31#41562# For unrelatedunc# libratedc ameras, this rec# nstr uc#r# n will be unique up to projec#939# y. Thus, projec#61 e rec#376#39162 of a set of lines and points may bec#40940 out linearly from three views.
Robust Detection of Degenerate Configurations whilst Estimating the Fundamental Matrix
, 1998
"... We present a new method for the detection of multiple solutions or degeneracy when estimating the Fundamental Matrix, with specific emphasis on robustness to data contamination (mismatches). The Fundamental Matrix encapsulates all the information on camera motion and internal parameters available f ..."
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Cited by 31 (3 self)
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We present a new method for the detection of multiple solutions or degeneracy when estimating the Fundamental Matrix, with specific emphasis on robustness to data contamination (mismatches). The Fundamental Matrix encapsulates all the information on camera motion and internal parameters available from image feature correspondences between two views. It is often used as a first step in structure from motion algorithms. If the set of correspondences is degenerate, then this structure cannot be accurately recovered and many solutions explain the data equally well. It is essential that we are alerted to such eventualities. As current feature matchers are very prone to mismatching the degeneracy detection method must also be robust to outliers. In this paper a definition of degeneracy is given and all two view nondegenerate and degenerate cases are catalogued in a logical way by introducing the language of varieties from algebraic geometry. It is then shown how each of the cases can be ro...
Linear projective reconstruction from matching tensors
 In British Machine Vision Conference
, 1996
"... ..."
Plane + Parallax, Tensors and Factorization
 In Proc. of ECCV
, 2000
"... Abstract. We study the special form that the general multiimage tensor formalism takes under the plane + parallax decomposition, including matching tensors and constraints, closure and depth recovery relations, and intertensor consistency constraints. Plane + parallax alignment greatly simplifies ..."
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Cited by 29 (1 self)
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Abstract. We study the special form that the general multiimage tensor formalism takes under the plane + parallax decomposition, including matching tensors and constraints, closure and depth recovery relations, and intertensor consistency constraints. Plane + parallax alignment greatly simplifies the algebra, and uncovers the underlying geometric content. We relate plane + parallax to the geometry of translating, calibrated cameras, and introduce a new parallaxfactorizing projective reconstruction method based on this. Initial plane + parallax alignment reduces the problem to a single rankone factorization of a matrix of rescaled parallaxes into a vector of projection centres and a vector of projective heights above the reference plane. The method extends to 3D lines represented by viapoints and 3D planes represented by homographies.