The Experts below are selected from a list of 34446 Experts worldwide ranked by ideXlab platform
Hanzi Wang - One of the best experts on this subject based on the ideXlab platform.
-
ICIP - AMSAC: An adaptive Robust Estimator for model fitting
2013 IEEE International Conference on Image Processing, 2013Co-Authors: Hanzi Wang, Jinlong Cai, Jianyu TangAbstract:In this paper, we firstly propose a novel Robust scale Estimator called AIKOSE. It can estimate the scale of inlier noises by adaptively selecting the optimal value of K in the IKOSE scale Estimator. Moreover, based on AIKOSE, we propose a novel Robust Estimator called AMSAC, which can fit a model without requiring a manually tuned threshold. In the experiments, we demonstrate the performance of AMSAC on line fitting and homography estimation by using both synthetic data and real images. Experimental results show that AM-SAC is more Robust than other competing Robust Estimators.
-
A Generalized Kernel Consensus-Based Robust Estimator
IEEE transactions on pattern analysis and machine intelligence, 2010Co-Authors: Hanzi Wang, Daniel J. Mirota, Gregory D. HagerAbstract:In this paper, we present a new adaptive-scale kernel consensus (ASKC) Robust Estimator as a generalization of the popular and state-of-the-art Robust Estimators such as random sample consensus (RANSAC), adaptive scale sample consensus (ASSC), and maximum kernel density Estimator (MKDE). The ASKC framework is grounded on and unifies these Robust Estimators using nonparametric kernel density estimation theory. In particular, we show that each of these methods is a special case of ASKC using a specific kernel. Like these methods, ASKC can tolerate more than 50 percent outliers, but it can also automatically estimate the scale of inliers. We apply ASKC to two important areas in computer vision, Robust motion estimation and pose estimation, and show comparative results on both synthetic and real data.
-
Robust motion estimation and structure recovery from endoscopic image sequences with an adaptive scale kernel consensus Estimator
Computer Vision and Pattern Recognition, 2008Co-Authors: Hanzi Wang, Daniel Mirota, Masaru Ishii, Gregory D. HagerAbstract:To correctly estimate the camera motion parameters and reconstruct the structure of the surrounding tissues from endoscopic image sequences, we need not only to deal with outliers (e.g., mismatches), which may involve more than 50% of the data, but also to accurately distinguish inliers (correct matches) from outliers. In this paper, we propose a new Robust Estimator, Adaptive Scale Kernel Consensus (ASKC), which can tolerate more than 50 percent outliers while automatically estimating the scale of inliers. With ASKC, we develop a reliable feature tracking algorithm. This, in turn, allows us to develop a complete system for estimating endoscopic camera motion and reconstructing anatomical structures from endoscopic image sequences. Preliminary experiments on endoscopic sinus imagery have achieved promising results.
-
mdpe a very Robust Estimator for model fitting and range image segmentation
International Journal of Computer Vision, 2004Co-Authors: Hanzi Wang, David SuterAbstract:In this paper, we propose a novel and highly Robust Estimator, called MDPE1 (Maximum Density Power Estimator). This Estimator applies nonparametric density estimation and density gradient estimation techniques in parametric estimation (“model fitting”). MDPE optimizes an objective function that measures more than just the size of the residuals. Both the density distribution of data points in residual space and the size of the residual corresponding to the local maximum of the density distribution, are considered as important characteristics in our objective function. MDPE can tolerate more than 85% outliers. Compared with several other recently proposed similar Estimators, MDPE has a higher Robustness to outliers and less error variance. We also present a new range image segmentation algorithm, based on a modified version of the MDPE (Quick-MDPE), and its performance is compared to several other segmentation methods. Segmentation requires more than a simple minded application of an Estimator, no matter how good that Estimator is: our segmentation algorithm overcomes several difficulties faced with applying a statistical Estimator to this task.
-
A model-based range image segmentation algorithm using a novel Robust Estimator
2003Co-Authors: Hanzi Wang, David SuterAbstract:This paper presents a novel range image segmentation algorithm based on a newly proposed Robust Estimator: Adaptive Scale Sample Consensus (ASSC) [28]. The proposed algorithm is a model-based top-down technique and directly extracts the required primitives (models) from the raw images. Compared with current popular methods (region-based and edge-based methods), the algorithm is very Robust to noisy or occluded data due to the adoption of the novel Robust Estimator ASSC. Using a hierarchical implementation, the proposed method is computationally efficient. Experimental results on real range images show that the proposed algorithm is attractive when compared with other state-of-the-art segmentation methods.
Luciano Silva - One of the best experts on this subject based on the ideXlab platform.
-
range image segmentation into planar and quadric surfaces using an improved Robust Estimator and genetic algorithm
Systems Man and Cybernetics, 2004Co-Authors: Paulo F U Gotardo, Olga Regina Pereira Bellon, Kim L Boyer, Luciano SilvaAbstract:This paper presents a novel range image segmentation method employing an improved Robust Estimator to iteratively detect and extract distinct planar and quadric surfaces. Our Robust Estimator extends M-Estimator Sample Consensus/Random Sample Consensus (MSAC/RANSAC) to use local surface orientation information, enhancing the accuracy of inlier/outlier classification when processing noisy range data describing multiple structures. An efficient approximation to the true geometric distance between a point and a quadric surface also contributes to effectively reject weak surface hypotheses and avoid the extraction of false surface components. Additionally, a genetic algorithm was specifically designed to accelerate the optimization process of surface extraction, while avoiding premature convergence. We present thorough experimental results with quantitative evaluation against ground truth. The segmentation algorithm was applied to three real range image databases and competes favorably against eleven other segmenters using the most popular evaluation framework in the literature. Our approach lends itself naturally to parallel implementation and application in real-time tasks. The method fits well into several of today's applications in man-made environments, such as target detection and autonomous navigation, for which obstacle detection, but not description or reconstruction, is required. It can also be extended to process point clouds resulting from range image registration.
-
range image segmentation by surface extraction using an improved Robust Estimator
Computer Vision and Pattern Recognition, 2003Co-Authors: Paulo F U Gotardo, Olga Regina Pereira Bellon, Luciano SilvaAbstract:The paper presents a novel range image segmentation algorithm based on planar surface extraction. The algorithm was applied to common range image databases and was favorably compared against seven other segmentation algorithms using a popular evaluation framework. The experimental results show that, as compared to the other methods, our algorithm presents a good performance in preserving small regions and edge locations when processing noisy images. Our main contribution is an improved Robust Estimator, derived from the RANSAC and MSAC Estimators, whose optimization process is accelerated by a genetic algorithm with a new set of parameters and operations designed to avoid premature convergence.
David Suter - One of the best experts on this subject based on the ideXlab platform.
-
mdpe a very Robust Estimator for model fitting and range image segmentation
International Journal of Computer Vision, 2004Co-Authors: Hanzi Wang, David SuterAbstract:In this paper, we propose a novel and highly Robust Estimator, called MDPE1 (Maximum Density Power Estimator). This Estimator applies nonparametric density estimation and density gradient estimation techniques in parametric estimation (“model fitting”). MDPE optimizes an objective function that measures more than just the size of the residuals. Both the density distribution of data points in residual space and the size of the residual corresponding to the local maximum of the density distribution, are considered as important characteristics in our objective function. MDPE can tolerate more than 85% outliers. Compared with several other recently proposed similar Estimators, MDPE has a higher Robustness to outliers and less error variance. We also present a new range image segmentation algorithm, based on a modified version of the MDPE (Quick-MDPE), and its performance is compared to several other segmentation methods. Segmentation requires more than a simple minded application of an Estimator, no matter how good that Estimator is: our segmentation algorithm overcomes several difficulties faced with applying a statistical Estimator to this task.
-
A model-based range image segmentation algorithm using a novel Robust Estimator
2003Co-Authors: Hanzi Wang, David SuterAbstract:This paper presents a novel range image segmentation algorithm based on a newly proposed Robust Estimator: Adaptive Scale Sample Consensus (ASSC) [28]. The proposed algorithm is a model-based top-down technique and directly extracts the required primitives (models) from the raw images. Compared with current popular methods (region-based and edge-based methods), the algorithm is very Robust to noisy or occluded data due to the adoption of the novel Robust Estimator ASSC. Using a hierarchical implementation, the proposed method is computationally efficient. Experimental results on real range images show that the proposed algorithm is attractive when compared with other state-of-the-art segmentation methods.
Kenji Fukumizu - One of the best experts on this subject based on the ideXlab platform.
-
NIPS - Trimmed Density Ratio Estimation
2017Co-Authors: Song Liu, Taiji Suzuki, Akiko Takeda, Kenji FukumizuAbstract:Density ratio estimation is a vital tool in both machine learning and statistical community. However, due to the unbounded nature of density ratio, the estimation proceudre can be vulnerable to corrupted data points, which often pushes the estimated ratio toward infinity. In this paper, we present a Robust Estimator which automatically identifies and trims outliers. The proposed Estimator has a convex formulation, and the global optimum can be obtained via subgradient descent. We analyze the parameter estimation error of this Estimator under high-dimensional settings. Experiments are conducted to verify the effectiveness of the Estimator.
-
Trimmed Density Ratio Estimation
arXiv: Machine Learning, 2017Co-Authors: Song Liu, Taiji Suzuki, Akiko Takeda, Kenji FukumizuAbstract:Density ratio estimation is a vital tool in both machine learning and statistical community. However, due to the unbounded nature of density ratio, the estimation procedure can be vulnerable to corrupted data points, which often pushes the estimated ratio toward infinity. In this paper, we present a Robust Estimator which automatically identifies and trims outliers. The proposed Estimator has a convex formulation, and the global optimum can be obtained via subgradient descent. We analyze the parameter estimation error of this Estimator under high-dimensional settings. Experiments are conducted to verify the effectiveness of the Estimator.
Xiaohua Zhou - One of the best experts on this subject based on the ideXlab platform.
-
double Robust Estimator of average causal treatment effect for censored medical cost data
Statistics in Medicine, 2016Co-Authors: Xuan Wang, Lauren A Beste, Marissa M Maier, Xiaohua ZhouAbstract:In observational studies, estimation of average causal treatment effect on a patient's response should adjust for confounders that are associated with both treatment exposure and response. In addition, the response, such as medical cost, may have incomplete follow-up. In this article, a double Robust Estimator is proposed for average causal treatment effect for right censored medical cost data. The Estimator is double Robust in the sense that it remains consistent when either the model for the treatment assignment or the regression model for the response is correctly specified. Double Robust Estimators increase the likelihood the results will represent a valid inference. Asymptotic normality is obtained for the proposed Estimator, and an Estimator for the asymptotic variance is also derived. Simulation studies show good finite sample performance of the proposed Estimator and a real data analysis using the proposed method is provided as illustration. Copyright © 2016 John Wiley & Sons, Ltd.
