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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 ..."
Abstract

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.
Fast Global Kernel Density Mode Seeking with Application to Localisation and Tracking
 In IEEE International Conference on Computer Vision
, 2005
"... We address the problem of seeking the global mode of a density function using the mean shift algorithm. Mean shift, like other gradient ascent optimisation methods, is susceptible to local maxima, and hence often fails to find the desired global maximum. In this work, we propose a multibandwidth me ..."
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Cited by 13 (1 self)
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We address the problem of seeking the global mode of a density function using the mean shift algorithm. Mean shift, like other gradient ascent optimisation methods, is susceptible to local maxima, and hence often fails to find the desired global maximum. In this work, we propose a multibandwidth mean shift procedure that avoids this problem, which we term annealed mean shift, as it shares similarities with the annealed importance sampling procedure. The bandwidth of the algorithm plays the same role as the temperature in annealing. We observe that the oversmoothed density function with a sufficiently large bandwidth is unimodal. Using a continuation principle, the influence of the global peak in the density function is introduced gradually. In this way the global maximum is more reliably located. Generally, the price of this annealinglike procedure is that more iterations are required. Since it is imperative that the computation complexity is minimal in realtime applications such as visual tracking. We propose an accelerated version of the mean shift algorithm. Compared with the conventional mean shift algorithm, annealed mean shift can significantly decrease the number of iterations required for convergence. The proposed algorithm is applied to the problems of visual tracking and object localisation. We empirically show on various data sets that the proposed algorithm can reliably find the true object location when the starting position of mean shift is far away from the global maximum, in contrast with the conventional mean shift algorithm that will usually get trapped in a spurious local maximum.
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
Robust Statistics for Computer Vision: Model fitting, Image Segmentation and Visual Motion Analysis
, 2004
"... ..."
Multibandwidth KernelBased Object Tracking
, 2010
"... Object tracking using Mean Shift (MS) has been attracting considerable attention recently. In this paper, we try to deal with one of its shortcoming. Mean shift is designed to find local maxima for tracking objects. Therefore, in large target movement between two consecutive frames, the local and ..."
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Cited by 1 (0 self)
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Object tracking using Mean Shift (MS) has been attracting considerable attention recently. In this paper, we try to deal with one of its shortcoming. Mean shift is designed to find local maxima for tracking objects. Therefore, in large target movement between two consecutive frames, the local and global modes are not the same as previous frames so that Mean Shift tracker may fail in tracking the desired object via localizing the global mode. To overcome this problem, a multibandwidth procedure is proposed to help conventional MS tracker reach the global mode of the density function using any staring points. This gradually smoothening procedure is called Multi Bandwidth Mean Shift (MBMS) which in fact smoothens the Kernel Function through a multiple kernelbased sampling procedure automatically. Since it is important for us to have less computational complexity for realtime applications, we try to decrease the number of iterations to reach the global mode. Based on our results, this proposed version of MS enables us to track an object with the same initial point much faster than conventional MS tracker.
Departament de Ciències Matemàtiques i Informàtica
"... Analytical methods for the study of color in ..."
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