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  • 1
    Electronic Resource
    Electronic Resource
    Image-guided decision support system for pathology (1999)
    Springer
    Machine vision and applications 11 (1999), S. 213-224 
    Details
    ISSN: 1432-1769
    Keywords: Key words:Content-based retrieval – Decision support system – Color segmentation – Information fusion – User interfaces
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract. We present a content-based image retrieval system that supports decision making in clinical pathology. The image-guided decision support system locates, retrieves, and displays cases which exhibit morphological profiles consistent to the case in question. It uses an image database containing 261 digitized specimens which belong to three classes of lymphoproliferative disorders and a class of healthy leukocytes. The reliability of the central module, the fast color segmenter, makes possible unsupervised on-line analysis of the query image and extraction of the features of interest: shape, area, and texture of the nucleus. The nuclear shape is characterized through similarity invariant Fourier descriptors, while the texture analysis is based on a multiresolution simultaneous autoregressive model. The system performance was assessed through ten-fold cross-validated classification and compared with that of a human expert. To facilitate a natural man-machine interface, speech recognition and voice feedback are integrated. Client-server communication is multithreaded, Internet-based, and provides access to supporting clinical records and video databases.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/s001380050104
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    Educational Credentials and Trust in the Labor Market (1996)
    Oxford, UK : Blackwell Publishing Ltd
    Kyklos 49 (1996), S. 0 
    Details
    ISSN: 1467-6435
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Sociology , Economics
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1467-6435.1996.tb01383.x
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    Paper (German National Licenses)
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  • 3
    Electronic Resource
    Electronic Resource
    Distribution Free Decomposition of Multivariate Data (1999)
    Springer
    Pattern analysis and applications 2 (1999), S. 22-30 
    Details
    ISSN: 1433-755X
    Keywords: Key words: Convergence; Gradient density estimation; Mean shift procedure; Mode seeking; Nonparametric cluster analysis; Range searching
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract: We present a practical approach to nonparametric cluster analysis of large data sets. The number of clusters and the cluster centres are automatically derived by mode seeking with the mean shift procedure on a reduced set of points randomly selected from the data. The cluster boundaries are delineated using a k-nearest neighbour technique. The proposed algorithm is stable and efficient, a 10,000 point data set being decomposed in only a few seconds. Complex clustering examples and applications are discussed, and convergence of the gradient ascent mean shift procedure is demonstrated for arbitrary distribution and cardinality of the data.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/s100440050011
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    Paper (German National Licenses)
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  • 4
    Electronic Resource
    Electronic Resource
    Robust regression methods for computer vision: A review (1991)
    Springer
    International journal of computer vision 6 (1991), S. 59-70 
    Details
    ISSN: 1573-1405
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: 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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00127126
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    Paper (German National Licenses)
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  • 5
    Electronic Resource
    Electronic Resource
    Heteroscedastic Regression in Computer Vision: Problems with Bilinear Constraint (2000)
    Springer
    International journal of computer vision 37 (2000), S. 127-150 
    Details
    ISSN: 1573-1405
    Keywords: heteroscedastic regression ; ellipse fitting ; epipolar constraint ; fundamental matrix ; uncalibrated camera
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We present an algorithm to estimate the parameters of a linear model in the presence of heteroscedastic noise, i.e., each data point having a different covariance matrix. The algorithm is motivated by the recovery of bilinear forms, one of the fundamental problems in computer vision which appears whenever the epipolar constraint is imposed, or a conic is fit to noisy data points. We employ the errors-in-variables (EIV) model and show why already at moderate noise levels most available methods fail to provide a satisfactory solution. The improved behavior of the new algorithm is due to two factors: taking into account the heteroscedastic nature of the errors arising from the linearization of the bilinear form, and the use of generalized singular value decomposition (GSVD) in the computations. The performance of the algorithm is compared with several methods proposed in the literature for ellipse fitting and estimation of the fundamental matrix. It is shown that the algorithm achieves the accuracy of nonlinear optimization techniques at much less computational cost.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008185619375
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    Paper (German National Licenses)
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  • 6
    Electronic Resource
    Electronic Resource
    Efficient Invariant Representations (1998)
    Springer
    International journal of computer vision 26 (1998), S. 137-152 
    Details
    ISSN: 1573-1405
    Keywords: invariance ; feature correspondence ; indexing ; geometric hashing
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: 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 p2-invariants which are insensitive to the labeling of the feature set. A general method to compute the p2-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 p2-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 p2-invariants as an indexing tool in object recognition systems may also be of interest.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1007944826230
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    Paper (German National Licenses)
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