Showing papers by "Peter Meer published in 1991"
TL;DR: The least-median-of-squares (LMedS) method, which yields the correct result even when half of the data is severely corrupted, is described and compared with the class of robust M-estimators.
Abstract: Regression analysis (fitting a model to noisy data) is a basic technique in computer vision, Robust regression methods that remain reliable in the presence of various types of noise are therefore of considerable importance. We review several robust estimation techniques and describe in detail the least-median-of-squares (LMedS) method. The method yields the correct result even when half of the data is severely corrupted. Its efficiency in the presence of Gaussian noise can be improved by complementing it with a weighted least-squares-based procedure. The high time-complexity of the LMedS algorithm can be reduced by a Monte Carlo type speed-up technique. We discuss the relationship of LMedS with the RANSAC paradigm and its limitations in the presence of noise corrupting all the data, and we compare its performance with the class of robust M-estimators. References to published applications of robust techniques in computer vision are also given.
653 citations
TL;DR: A clustering algorithm based on the minimum volume ellipsoid (MVE) robust estimator is proposed that was successfully applied to several computer vision problems formulated in the feature space paradigm: multithresholding of gray level images, analysis of the Hough space, and range image segmentation.
Abstract: A clustering algorithm based on the minimum volume ellipsoid (MVE) robust estimator is proposed. The MVE estimator identifies the least volume region containing h percent of the data points. The clustering algorithm iteratively partitions the space into clusters without prior information about their number. At each iteration, the MVE estimator is applied several times with values of h decreasing from 0.5. A cluster is hypothesized for each ellipsoid. The shapes of these clusters are compared with shapes corresponding to a known unimodal distribution by the Kolmogorov-Smirnov test. The best fitting cluster is then removed from the space, and a new iteration starts. Constrained random sampling keeps the computation low. The clustering algorithm was successfully applied to several computer vision problems formulated in the feature space paradigm: multithresholding of gray level images, analysis of the Hough space, and range image segmentation. >
290 citations
TL;DR: A novel multiresolution image analysis technique based on hierarchies of irregular tessellations generated in parallel by independent stochastic processes is presented, which adapted to the image content and artifacts of rigid resolution reduction are avoided.
Abstract: A novel multiresolution image analysis technique based on hierarchies of irregular tessellations generated in parallel by independent stochastic processes is presented. Like traditional image pyramids these hierarchies are constructed in a number of steps on the order of log(image-size) steps. However, the structure of a hierarchy is adapted to the image content and artifacts of rigid resolution reduction are avoided. Two applications of these techniques are presented: connected component analysis of labeled images and segmentation of gray level images. In labeled images, every connected component is reduced to a separate root, with the adjacency relations among the components also extracted. In gray level images the output is a segmentation of the image into a small number of classes as well as the adjacency graph of the classes. >
183 citations
TL;DR: A hierarchical implementation of an edge-preserving smoothing algorithm on the 2 x 2 pyramid structure that eliminates artifacts of region-based smoothing by pixelwise averaging over a set of outputs obtained with the input image shifted within the 8 x 8 block of the level three parent.
10 citations
TL;DR: Eight statistical textural features based on co-occurrence matrices are computed on the resampled data, and are used to discriminate between two classes of natural surfaces consisting of pebbles of different sizes lying on a plane.
5 citations
01 Feb 1991
TL;DR: A new approach is developed which preserves the robustness of LMedS but avoids its artifacts in the presence of noise, which is important in computer vision applications.
Abstract: We describe the least median of squares (LMedS) robust estimator which identifies the surface corresponding to the absolute majority of the data points. However when all the data points are corrupted by noise LMedS may fail. This is the case in computer vision applications and we have developed a new approach which preserves the robustness of LMedS but avoids its artifacts in the presence of noise.
2 citations