Showing papers by "Peter Meer published in 1998"
TL;DR: A novel image segmentation technique using the robust, adaptive least kth order squares (ALKS) estimator which minimizes the kth Order statistics of the squares of residuals.
Abstract: We propose a novel image segmentation technique using the robust, adaptive least kth order squares (ALKS) estimator which minimizes the kth order statistics of the squares of residuals. The optimal value of k is determined from the data, and the procedure detects the homogeneous surface patch representing the relative majority of the pixels. The ALKS shows a better tolerance to structured outliers than other recently proposed similar techniques. The performance of the new, fully autonomous, range image segmentation algorithm is compared to several other methods.
164 citations
TL;DR: The class of projective/permutation p2-invariants which are insensitive to the labeling of the feature set are introduced, and a general method to compute the p2 -invariant of a point set (or of its dual) in the n-dimensional projective space is given.
Abstract: Invariant representations are frequently used in computer vision algorithms to eliminate the effect of an unknown transformation of the data. These representations, however, depend on the order in which the features are considered in the computations. We introduce the class of projective/permutation p^2-invariants which are insensitive to the labeling of the feature set. A general method to compute the p^2-invariant of a point set (or of its dual) in the n-dimensional projective space is given. The one-to-one mapping between n + 3 points and the components of their p^2-invariant representation makes it possible to design correspondence algorithms with superior tolerance to positional errors. An algorithm for coplanar points in projective correspondence is described as an application, and its performance is investigated. The use of p^2-invariants as an indexing tool in object recognition systems may also be of interest.
59 citations
19 Oct 1998
TL;DR: The prototype of an image understanding based system to support decision making in clinical pathology is demonstrated, employing all four major low level vision queues in content-based retrieval of visual information.
Abstract: The prototype of a system to assist the physicians in differential diagnosis of lymphoproliferative disorders of blood cells from digitized specimens is presented. The user selects the region of interest (ROI) in the image which is then analyzed with a fast, robust color segmenter. Queries in a database of validated cases can be formulated in terms of shape (similarity invariant Fourier descriptors), texture (multiresolution simultaneous autoregressive model), color (L*u*/spl upsi/* space), and area, derived from the delineated ROI. The uncertainty of the segmentation process (obtained through a numerical method) determines the accuracy of shape description (number of Fourier harmonics). Ten-fold cross-validated classification over a database of 261 color 640/spl times/480 images was implemented to assess the system performance. The ground truth was obtained through immunophenotyping by flow cytometry. To provide a natural man-machine interface, most input commands are bimodal: either using the mouse or by voice. A speech synthesizer provides feedback to the user. All the employed computational modules are context independent and thus the same system can be used in a large variety of application domains.
42 citations
16 Aug 1998
TL;DR: A prototype system performing analysis, indexing and retrieval of pathology images to assist physicians in differential diagnosis of lymphoproliferative disorders is presented and robust color segmentation is used to automatically analyse regions of interest in images of leukocytes.
Abstract: A prototype system performing analysis, indexing and retrieval of pathology images to assist physicians in differential diagnosis of lymphoproliferative disorders is presented. Robust color segmentation is used to automatically analyse regions of interest in images of leukocytes. The shape of leukocyte nuclei, described through similarity invariant shape descriptors, represents the main attribute in the search query. Monte Carlo tests for stability and goal-directed evaluations of the system performance are also shown.
29 citations
TL;DR: A practical approach to nonparametric cluster analysis of large data sets is presented, allowing the cluster decomposition of a 10000 point data set in only a few seconds.
Abstract: A practical approach to nonparametric cluster analysis of large data sets is presented. The number of clusters and the cluster centers are derived by applying the mean shift procedure on a reduced set of points randomly selected from the data. The cluster boundaries are delineated using a k-nearest neighbor technique. The resulting algorithm is stable and efficient, allowing the cluster decomposition of a 10000 point data set in only a few seconds. Complex clustering examples and applications are discussed.
15 citations
01 Mar 1998
TL;DR: This work states that there is a wide gap between what performance assessment using simple, synthetic data predicts and what is obtained when the same algorithms are applied to real data.
Abstract: Performance evaluation is a difficult and very challenging task. In spite of many discussions in the literature, e.g., (Haralick et al., 1994), and well understood goals, e.g., (Christensen and Forstner, 1997; Haralick, 1994), there is a wide gap between what performance assessment using simple, synthetic data predicts and what is obtained when the same algorithms are applied to real data.
6 citations
3 citations
TL;DR: In this paper, high breakdown point location estimators are employed to analyze image stacks under the piecewise constant image structure model, and the segmentation algorithm first determines the most reliable seed regions which are then used in a region growing procedure supported by local evidence.
Abstract: Robust high breakdown point location estimators are employed to analyze image stacks under the piecewise constant image structure model. To reduce the effect of bias along the Z axis, the class parameters are extracted using three consecutive slices. The segmentation algorithm first determines the most reliable seed regions which are then used in a region-growing procedure supported by local evidence. The robustness and stability of the proposed technique is shown with both synthetic and real data, the latter consisting of two MRI sets.
01 Jan 1998
TL;DR: The prototype of a system to assist the physicians in differential diagnosis of lymphoproliferative disorders of blood cells from digitized specimens is presented and all the employed computational modules are context independent and thus the same system can be used in a large variety of application domains.
Abstract: The prototype of a system to assist the physicians in differential diagnosis of lymphoproliferative disorders of blood cells from digitized specimens is presented. The user selects the region of interest (ROI) in the image which is then analyzed with a fast, robust color segmentel: Queries in a database of validated cases can be formulated in terms of shape (similarity invariant Fourier descriptors), texture (multiresolution simultaneous autoregressive model), color (L*u*v* space), and area, derived from the delineated ROI. The uncertainty of the segmentation process (obtained through a numerical method) determines the accuracy of shape description (number of Fourier harmonics). Tenfold cross-validated classification over a database of 261 color 640 x 480 images was implemented to assess the system performance. The ground truth was obtained through immunophenotjping by flow cytomety. To provide a natural man-machine interface, most input commands are bimodal: either using the mouse or by voice. A speech synthesizerprovides feedback to the user: All the employed computational modules are context independent and thus the same system can be used in a large variety of application domains.