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A theory of shape by space carving
 In Proceedings of the 7th IEEE International Conference on Computer Vision (ICCV99), volume I, pages 307– 314, Los Alamitos, CA
, 1999
"... In this paper we consider the problem of computing the 3D shape of an unknown, arbitrarilyshaped scene from multiple photographs taken at known but arbitrarilydistributed viewpoints. By studying the equivalence class of all 3D shapes that reproduce the input photographs, we prove the existence of a ..."
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Cited by 469 (14 self)
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In this paper we consider the problem of computing the 3D shape of an unknown, arbitrarilyshaped scene from multiple photographs taken at known but arbitrarilydistributed viewpoints. By studying the equivalence class of all 3D shapes that reproduce the input photographs, we prove the existence of a special member of this class, the photo hull, that (1) can be computed directly from photographs of the scene, and (2) subsumes all other members of this class. We then give a provablycorrect algorithm, called Space Carving, for computing this shape and present experimental results on complex realworld scenes. The approach is designed to (1) build photorealistic shapes that accurately model scene appearance from a wide range of viewpoints, and (2) account for the complex interactions between occlusion, parallax, shading, and their effects on arbitrary views of a 3D scene. 1.
The development and comparison of robust methods for estimating the fundamental matrix
 International Journal of Computer Vision
, 1997
"... Abstract. This paper has two goals. The first is to develop a variety of robust methods for the computation of the Fundamental Matrix, the calibrationfree representation of camera motion. The methods are drawn from the principal categories of robust estimators, viz. case deletion diagnostics, Mest ..."
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Cited by 225 (10 self)
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Abstract. This paper has two goals. The first is to develop a variety of robust methods for the computation of the Fundamental Matrix, the calibrationfree representation of camera motion. The methods are drawn from the principal categories of robust estimators, viz. case deletion diagnostics, Mestimators and random sampling, and the paper develops the theory required to apply them to nonlinear orthogonal regression problems. Although a considerable amount of interest has focussed on the application of robust estimation in computer vision, the relative merits of the many individual methods are unknown, leaving the potential practitioner to guess at their value. The second goal is therefore to compare and judge the methods. Comparative tests are carried out using correspondences generated both synthetically in a statistically controlled fashion and from feature matching in real imagery. In contrast with previously reported methods the goodness of fit to the synthetic observations is judged not in terms of the fit to the observations per se but in terms of fit to the ground truth. A variety of error measures are examined. The experiments allow a statistically satisfying and quasioptimal method to be synthesized, which is shown to be stable with up to 50 percent outlier contamination, and may still be used if there are more than 50 percent outliers. Performance bounds are established for the method, and a variety of robust methods to estimate the standard deviation of the error and covariance matrix of the parameters are examined. The results of the comparison have broad applicability to vision algorithms where the input data are corrupted not only by noise but also by gross outliers.
Robust parameter estimation in computer vision
 SIAM Reviews
, 1999
"... Abstract. Estimation techniques in computer vision applications must estimate accurate model parameters despite smallscale noise in the data, occasional largescale measurement errors (outliers), and measurements from multiple populations in the same data set. Increasingly, robust estimation techni ..."
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Cited by 130 (10 self)
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Abstract. Estimation techniques in computer vision applications must estimate accurate model parameters despite smallscale noise in the data, occasional largescale measurement errors (outliers), and measurements from multiple populations in the same data set. Increasingly, robust estimation techniques, some borrowed from the statistics literature and others described in the computer vision literature, have been used in solving these parameter estimation problems. Ideally, these techniques should effectively ignore the outliers and measurements from other populations, treating them as outliers, when estimating the parameters of a single population. Two frequently used techniques are leastmedian of
A Robust Competitive Clustering Algorithm with Applications in Computer Vision
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1998
"... This paper addresses three major issues associated with conventional partitional clustering, namely, sensitivity to initialization, difficulty in determining the number of clusters, and sensitivity to noise and outliers. The proposed Robust Competitive Agglomeration (RCA) algorithm starts with a lar ..."
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Cited by 86 (3 self)
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This paper addresses three major issues associated with conventional partitional clustering, namely, sensitivity to initialization, difficulty in determining the number of clusters, and sensitivity to noise and outliers. The proposed Robust Competitive Agglomeration (RCA) algorithm starts with a large number of clusters to reduce the sensitivity to initialization, and determines the actual number of clusters by a process of competitive agglomeration. Noise immunity is achieved by incorporating concepts from robust statistics into the algorithm. RCA assigns two different sets of weights for each data point: the first set of constrained weights represents degrees of sharing, and is used to create a competitive environment and to generate a fuzzy partition of the data set. The second set corresponds to robust weights, and is used to obtain robust estimates of the cluster prototypes. By choosing an appropriate distance measure in the objective function, RCA can be used to find a...
A variable window approach to early vision
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1998
"... Abstract—Early vision relies heavily on rectangular windows for tasks such as smoothing and computing correspondence. While rectangular windows are efficient, they yield poor results near object boundaries. We describe an efficient method for choosing an arbitrarily shaped connected window, in a man ..."
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Cited by 48 (3 self)
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Abstract—Early vision relies heavily on rectangular windows for tasks such as smoothing and computing correspondence. While rectangular windows are efficient, they yield poor results near object boundaries. We describe an efficient method for choosing an arbitrarily shaped connected window, in a manner that varies at each pixel. Our approach can be applied to several problems, including image restoration and visual correspondence. It runs in linear time, and takes a few seconds on traditional benchmark images. Performance on both synthetic and real imagery appears promising. Index Terms—Image restoration, motion, stereo, adaptive windows, visual correspondence.
Capturing Articulated Human Hand Motion: A DivideandConquer Approach
 In Proc. 7th Int. Conf. on Computer Vision, volume I
, 1999
"... The use of human hand as a natural interface device serves as a motivating force for research in the modeling, analyzing and capturing of the motion of articulated hand. Modelbased hand motion capturing can be formulated as a large nonlinear programming problem, but this approach is plagued by loca ..."
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Cited by 43 (13 self)
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The use of human hand as a natural interface device serves as a motivating force for research in the modeling, analyzing and capturing of the motion of articulated hand. Modelbased hand motion capturing can be formulated as a large nonlinear programming problem, but this approach is plagued by local minima. An alternative way is to use analysisbysynthesis by searching a huge space, but the results are rough and the computation expensive. In this paper, articulated hand motion is decoupled, a new twostep iterative modelbased algorithm is proposed to capture articulated human hand motion, and a proof of convergence of this iterative algorithm is also given. In our proposed work, the decoupled global hand motion and local finger motion are parameterized by 3D hand pose and the state of the hand respectively. Hand pose determination is formulated as a least median of squares (LMS) problem rather than the nonrobust least squares ...
Robust Adaptive Segmentation of Range Images
, 1996
"... We propose a novel image segmentation technique using the robust, adaptive least kth order squares (ALKS) estimator which minimizes the kth order statistics of the squared of residuals. The optimal value of k is determined from the data and the procedure detects the homogeneous surface patch repre ..."
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Cited by 42 (2 self)
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We propose a novel image segmentation technique using the robust, adaptive least kth order squares (ALKS) estimator which minimizes the kth order statistics of the squared of residuals. The optimal value of k is determined from the data and the procedure detects the homogeneous surface patch representing the relative majority of the pixels. The ALKS shows a better tolerance to structured outliers than other recently proposed similar techniques: Minimize the Probability of Randomness (MINPRAN) and Residual Consensus (RESC). The performance of the new, fully autonomous, range image segmentation algorithm is compared to several other methods.
Robust adaptivescale parametric model estimation for computer vision
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2004
"... Robust model fitting essentially requires the application of two estimators. The first is an estimator for the values of the model parameters. The second is an estimator for the scale of the noise in the (inlier) data. Indeed, we propose two novel robust techniques: the TwoStep Scale estimator (TSS ..."
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Cited by 34 (6 self)
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Robust model fitting essentially requires the application of two estimators. The first is an estimator for the values of the model parameters. The second is an estimator for the scale of the noise in the (inlier) data. Indeed, we propose two novel robust techniques: the TwoStep Scale estimator (TSSE) and the Adaptive Scale Sample Consensus (ASSC) estimator. TSSE applies nonparametric density estimation and density gradient estimation techniques, to robustly estimate the scale of the inliers. The ASSC estimator combines Random Sample Consensus (RANSAC) and TSSE: using a modified objective function that depends upon both the number of inliers and the corresponding scale. ASSC is very robust to discontinuous signals and data with multiple structures, being able to tolerate more than 80 % outliers. The main advantage of ASSC over RANSAC is that prior knowledge about the scale of inliers is not needed. ASSC can simultaneously estimate the parameters of a model and the scale of the inliers belonging to that model. Experiments on synthetic data show that ASSC has better robustness to heavily corrupted data than Least Median Squares (LMedS), Residual Consensus (RESC), and Adaptive Least K’th order Squares (ALKS). We also apply ASSC to two fundamental computer vision tasks: range image segmentation and robust fundamental matrix estimation. Experiments show very promising results.
Robust detection of degenerate configurations while estimating the fundamental matrix
 Comput. Vis. Image Understanding
, 1998
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Bias in Robust Estimation Caused by Discontinuities and Multiple Structures
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1997
"... When fitting models to data containing multiple structures, such as when fitting surface patches to data taken from a neighborhood that includes a range discontinuity, robust estimators must tolerate both gross outliers and pseudo outliers. Pseudo outliers are outliers to the structure of interest, ..."
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Cited by 32 (1 self)
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When fitting models to data containing multiple structures, such as when fitting surface patches to data taken from a neighborhood that includes a range discontinuity, robust estimators must tolerate both gross outliers and pseudo outliers. Pseudo outliers are outliers to the structure of interest, but inliers to a different structure. They differ from gross outliers because of their coherence. Such data occurs frequently in computer vision problems, including motion estimation, model fitting and range data analysis. The focus in this paper is the problem of fitting surfaces near discontinuities in range data. To characterize the performance of least median of squares, least trimmed squares, Mestimators, Hough transforms, RANSAC, and MINPRAN on this type of data, the "pseudo outlier bias" metric is developed using techniques from the robust statistics literature, and it is used to study the error in robust fits caused by distributions modeling various types of discontinuities. The resu...