Showing papers by "Peter Meer published in 2005"

Unsupervised segmentation based on robust estimation and color active contour models

Abstract: One of the most commonly used clinical tests performed today is the routine evaluation of peripheral blood smears. In this paper, we investigate the design, development, and implementation of a robust color gradient vector flow (GVF) active contour model for performing segmentation, using a database of 1791 imaged cells. The algorithms developed for this research operate in Luv color space, and introduce a color gradient and L/sub 2/E robust estimation into the traditional GVF snake. The accuracy of the new model was compared with the segmentation results using a mean-shift approach, the traditional color GVF snake, and several other commonly used segmentation strategies. The unsupervised robust color snake with L/sub 2/E robust estimation was shown to provide results which were superior to the other unsupervised approaches, and was comparable with supervised segmentation, as judged by a panel of human experts.

Simultaneous multiple 3D motion estimation via mode finding on Lie groups

Abstract: We propose a new method to estimate multiple rigid motions from noisy 3D point correspondences in the presence of outliers. The method does not require prior specification of number of motion groups and estimates all the motion parameters simultaneously. We start with generating samples from the rigid motion distribution. The motion parameters are then estimated via mode finding operations on the sampled distribution. Since rigid motions do not lie on a vector space, classical statistical methods can not be used for mode finding. We develop a mean shift algorithm which estimates modes of the sampled distribution using the Lie group structure of the rigid motions. We also show that proposed mean shift algorithm is general and can be applied to any distribution having a matrix Lie group structure. Experimental results on synthetic and real image data demonstrate the superior performance of the algorithm.

A Bayesian Approach to Background Modeling

Abstract: Learning background statistics is an essential task for several visual surveillance applications such as incident detection and traf.c management. In this paper, we propose a new method for modeling background statistics of a dynamic scene. Each pixel is represented with layers of Gaussian distributions. Using recursive Bayesian learning, we estimate the probability distribution of mean and covariance of each Gaussian. The proposed algorithm preserves the multimodality of the background and estimates the number of necessary layers for representing each pixel. We compare our results with the Gaussian mixture background model. Experiments conducted on synthetic and video data demonstrate the superior performance of the proposed approach.

Heteroscedastic Projection Based M-Estimators

Abstract: Robust regression methods, such as RANSAC, suffer from a sensitivity to the scale parameter used for generating the inlier-outlier dichotomy. Projection based M-estimators (pbM) offer a solution to this by reframing the regression problem in a projection pursuit framework. In this paper we modify the pbM formulation to obtain an improved pbM algorithm. Furthermore, the modified algorithm is easily generalized to handle heteroscedastic data . The superior performance of heteroscedastic pbM, as compared to simple pbM, is experimentally verified.

A Balanced Approach to 3D Tracking from Image Streams

Abstract: Estimation of camera pose is an integral part of augmented reality systems. Vision-based methods offer a flexible and accurate method for this estimation. Current vision based methods rely on markers to reduce the computation and increase robustness of the pose estimation. However, this limits the algorithm's applicability while being expensive since the markers also require maintenance. Alternatively, reconstructed scene features can be used for pose estimation but this can lead to a loss of accuracy. To avoid this we propose a two-stage balanced tracking method which does not require any visual markers in the scene. The first stage of our method is based on the sequential recovery of structure from motion which allows the system to learn the scene from a few frames in which the markers are visible. In the next stage, the learned features are used for camera tracking. The system ensures greater accuracy and reduces error drift due to its use of the HEIV estimator which is provably unbiased to the first degree. We also make use of a novel method for the detection and removal of outliers which are unavoidable in such systems. The experiments show the superiority of our method when compared to a nonlinear method based on Levenberg-Marquardt minimization.