Results 1  10
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39
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 45 (7 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.
Integrating multiple model views for object recognition
 In IEEE Conference on Computer Vision and Pattern Recognition
, 2004
"... We present a new approach to appearancebased object recognition, which captures the relationships between multiple model views and exploits them to improve recognition performance. The basic building block are local, viewpoint invariant regions. We propose an efficient algorithm for partitioning a ..."
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Cited by 41 (6 self)
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We present a new approach to appearancebased object recognition, which captures the relationships between multiple model views and exploits them to improve recognition performance. The basic building block are local, viewpoint invariant regions. We propose an efficient algorithm for partitioning a set of region matches into groups lying on smooth surfaces (GAMs). During modeling, the model views are connected by a large number of regiontracks, each aggregating image regions of a single physical region across the views. At recognition time, GAMs are constructed matching a test image to each model view. The consistency of configurations of GAMs is measured by exploiting the model connections. The most consistent configuration, covering the object as completely as possible is found by a genetic algorithm. Introducing GAMs as an intermediate grouping level facilitates decisionmaking and improves discriminative power. As a complementary application, we introduce a novel GAMbased twoview filter and demonstrate its effectiveness in recovering correct matches in the presence of up to 96 % mismatches. 1.
Estimation of nonlinear errorsinvariables models for computer vision applications
 IEEE Trans. Patt. Anal. Mach. Intell
, 2006
"... Abstract—In an errorsinvariables (EIV) model, all the measurements are corrupted by noise. The class of EIV models with constraints separable into the product of two nonlinear functions, one solely in the variables and one solely in the parameters, is general enough to represent most computer visi ..."
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Cited by 33 (6 self)
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Abstract—In an errorsinvariables (EIV) model, all the measurements are corrupted by noise. The class of EIV models with constraints separable into the product of two nonlinear functions, one solely in the variables and one solely in the parameters, is general enough to represent most computer vision problems. We show that the estimation of such nonlinear EIV models can be reduced to iteratively estimating a linear model having point dependent, i.e., heteroscedastic, noise process. Particular cases of the proposed heteroscedastic errorsinvariables (HEIV) estimator are related to other techniques described in the vision literature: the Sampson method, renormalization, and the fundamental numerical scheme. In a wide variety of tasks, the HEIV estimator exhibits the same, or superior, performance as these techniques and has a weaker dependence on the quality of the initial solution than the LevenbergMarquardt method, the standard approach toward estimating nonlinear models. Index Terms—Nonlinear least squares, heteroscedastic regression, camera calibration, 3D rigid motion, uncalibrated vision. 1 MODELING COMPUTER VISION PROBLEMS SOLVING most computer vision problems requires the estimation of a set of parameters from noisy measurements using a statistical model. A statistical model provides a mathematical description of a problem in terms of a constraint equation relating the measurements to the
Robust fitting of multiple structures: The statistical learning approach
 In ICCV
, 2009
"... We propose an unconventional but highly effective approach to robust fitting of multiple structures by using statistical learning concepts. We design a novel Mercer kernel for the robust estimation problem which elicits the potential of two points to have emerged from the same underlying structure. ..."
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Cited by 24 (6 self)
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We propose an unconventional but highly effective approach to robust fitting of multiple structures by using statistical learning concepts. We design a novel Mercer kernel for the robust estimation problem which elicits the potential of two points to have emerged from the same underlying structure. The Mercer kernel permits the application of wellgrounded statistical learning methods, among which nonlinear dimensionality reduction, principal component analysis and spectral clustering are applied for robust fitting. Our method can remove gross outliers and in parallel discover the multiple structures present. It functions well under severe outliers (more than 90 % of the data) and considerable inlier noise without requiring elaborate manual tuning or unrealistic prior information. Experiments on synthetic and real problems illustrate the superiority of the proposed idea over previous methods. 1.
Balanced exploration and exploitation model search for efficient epipolar geometry estimation
 In ECCV
, 2006
"... Abstract. The estimation of the epipolar geometry is especially difficult where the putative correspondences include a low percentage of inlier correspondences and/or a large subset of the inliers is consistent with a degenerate configuration of the epipolar geometry that is totally incorrect. This ..."
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Cited by 20 (1 self)
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Abstract. The estimation of the epipolar geometry is especially difficult where the putative correspondences include a low percentage of inlier correspondences and/or a large subset of the inliers is consistent with a degenerate configuration of the epipolar geometry that is totally incorrect. This work presents the Balanced Exploration and Exploitation Model Search (BEEM) algorithm that works very well especially for these difficult scenes. The BEEM algorithm handles the above two difficult cases in a unified manner. The algorithm includes the following main features: (1) Balanced use of three search techniques: global random exploration, local exploration near the current best solution and local exploitation to improve the quality of the model. (2) Exploits available prior information to accelerate the search process. (3) Uses the best found model to guide the search process, escape from degenerate models and to define an efficient stopping criterion. (4) Presents a simple and efficient method to estimate the epipolar geometry from two SIFT correspondences. (5) Uses the localitysensitive hashing (LSH) approximate nearest neighbor algorithm for fast putative correspondences generation. The resulting algorithm when tested on real images with or without degenerate configurations gives quality estimations and achieves significant speedups compared to the state of the art algorithms! 1
Robust 6dof motion estimation for nonoverlapping, multicamera systems
 in IEEE Workshop on Applications of Computer Vision
"... This paper introduces a novel, robust approach for 6DOF motion estimation of a multicamera system with nonoverlapping views. The proposed approach is able to solve the pose estimation, including scale, for a two camera system with nonoverlapping views. In contrast to previous approaches, it degra ..."
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Cited by 14 (9 self)
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This paper introduces a novel, robust approach for 6DOF motion estimation of a multicamera system with nonoverlapping views. The proposed approach is able to solve the pose estimation, including scale, for a two camera system with nonoverlapping views. In contrast to previous approaches, it degrades gracefully if the motion is close to degenerate. For degenerate motions the technique estimates the remaining 5DOF. The proposed technique is evaluated on real and synthetic sequences. 1.
The modified pbMestimator method and a runtime analysis technique for the ransac family
 in Proc. IEEE Conf. on Computer Vision and Pattern Recognition
, 2005
"... Robust regression techniques are used today in many computer vision algorithms. Chen and Meer recently presented a new robust regression technique named the projection based Mestimator. Unlike other methods in the RANSAC family of techniques, where performance depends on a user supplied scale param ..."
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Cited by 12 (2 self)
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Robust regression techniques are used today in many computer vision algorithms. Chen and Meer recently presented a new robust regression technique named the projection based Mestimator. Unlike other methods in the RANSAC family of techniques, where performance depends on a user supplied scale parameter, in the pbMestimator technique this scale parameter is estimated automatically from the data using kernel smoothing density estimation. In this work we improve the performance of the pbMestimator by changing its cost function. Replacing the cost function of the pbMestimator with the changed one yields the modified pbMestimator. The cost function of the modified pbMestimator is more stable relative to the scale parameter and is also a better classifier. Thus we get a more robust and effective technique. A new general method to estimate the runtime of robust regression algorithms is proposed. Using it we show, that the modified pbMestimator runs 23 times faster than the pbMestimator. Experimental results of fundamental matrix estimation are presented demonstrating the correctness of the proposed analysis method and the advantages of the modified pbMestimator. 1
Robust fitting by adaptivescale residual consensus
 In ECCV
, 2004
"... Abstract. Computer vision tasks often require the robust fit of a model to some data. In a robust fit, two major steps should be taken: i) robustly estimate the parameters of a model, and ii) differentiate inliers from outliers. We propose a new estimator called AdaptiveScale Residual Consensus (AS ..."
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Cited by 11 (0 self)
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Abstract. Computer vision tasks often require the robust fit of a model to some data. In a robust fit, two major steps should be taken: i) robustly estimate the parameters of a model, and ii) differentiate inliers from outliers. We propose a new estimator called AdaptiveScale Residual Consensus (ASRC). ASRC scores a model based on both the residuals of inliers and the corresponding scale estimate determined by those inliers. ASRC is very robust to multiplestructural data containing a high percentage of outliers. Compared with RANSAC, ASRC requires no predetermined inlier threshold as it can simultaneously estimate the parameters of a model and the scale of inliers belonging to that model. Experiments show that ASRC has better robustness to heavily corrupted data than other robust methods. Our experiments address two important computer vision tasks: range image segmentation and fundamental matrix calculation. However, the range of potential applications is much broader than these. 1
Projection Based MEstimators
, 2009
"... Random Sample Consensus (RANSAC) is the most widely used robust regression algorithm in computer vision. However, RANSAC has a few drawbacks which make it difficult to use for practical applications. Some of these problems have been addressed through improved sampling algorithms or better cost funct ..."
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Cited by 11 (3 self)
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Random Sample Consensus (RANSAC) is the most widely used robust regression algorithm in computer vision. However, RANSAC has a few drawbacks which make it difficult to use for practical applications. Some of these problems have been addressed through improved sampling algorithms or better cost functions, but an important difficulty still remains. The algorithm is not user independent, and requires knowledge of the scale of the inlier noise. We propose a new robust regression algorithm, the projection based Mestimator (pbM). The pbM algorithm is derived by building a connection to the theory of kernel density estimation and this leads to an improved cost function, which gives better performance. Furthermore, pbM is user independent and does not require any knowledge of the scale of noise corrupting the inliers. We propose a general framework for the pbM algorithm which can handle heteroscedastic data and multiple linear constraints on each data point through the use of Grassmann manifold theory. The performance of pbM is compared with RANSAC and MEstimator Sample Consensus (MSAC) on various real problems. It is shown that pbM gives better results than RANSAC and MSAC in spite of being user independent.
Subspace estimation using projection based Mestimators over Grassmann manifolds
 in Proc. European Conf. on Computer Vision
, 2006
"... Abstract. We propose a solution to the problem of robust subspace estimation using the projection based Mestimator. The new method handles more outliers than inliers, does not require a user defined scale of the noise affecting the inliers, handles noncentered data and nonorthogonal subspaces. Othe ..."
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Cited by 10 (6 self)
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Abstract. We propose a solution to the problem of robust subspace estimation using the projection based Mestimator. The new method handles more outliers than inliers, does not require a user defined scale of the noise affecting the inliers, handles noncentered data and nonorthogonal subspaces. Other robust methods like RANSAC, use an input for the scale, while methods for subspace segmentation, like GPCA, are not robust. Synthetic data and three real cases of multibody factorization show the superiority of our method, in spite of user independence. 1