Motivated by the requirements of the present archaeology, we are developing an automated system for archaeological classification of ceramics. The basis for classification and reconstruction of ceramics is the profile...
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ISBN:
(纸本)1581134479
Motivated by the requirements of the present archaeology, we are developing an automated system for archaeological classification of ceramics. The basis for classification and reconstruction of ceramics is the profile, which is the cross-section of the fragment in the direction of the rotational axis of symmetry, and can be represented by a closed curve in the plane. This paper compares and combines several methods for interpolation and approximation of a closed curve by B-splines in the plane. The closed curve, representing the profile, is divided into several parts for which the most accurate method is selected. All the interpolation and approximation methods are compared on the provided data with respect to the achieved precision and 'complexity' of the curve description. The graphical output of the program suggests to the archaeologists, which combination of these methods gives the best representation of the reconstructed profile from the data under the smallest possible error and the simplest possible spline representation.
We develop a framework for vector quantization networks based on the minimum description length (MDL) principle. This MDL framework is used to derive conditions for the removal of superfluous units from the network. W...
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We develop a framework for vector quantization networks based on the minimum description length (MDL) principle. This MDL framework is used to derive conditions for the removal of superfluous units from the network. We design a computationally efficient algorithm for finding the optimal number of reference vectors as well as their positions. We illustrate our approach on 2D clustering problems and present applications on image coding.
In this paper we study the problem of missing features and the issues of robustness of subspace classification methods. We propose a new robust method for subspace classification which can cope with missing features a...
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In this paper we study the problem of missing features and the issues of robustness of subspace classification methods. We propose a new robust method for subspace classification which can cope with missing features and/or outliers. The main idea of our method is to use a robust projection of the patterns onto a subspace. We demonstrate our approach on cervicomotography data and compare our results to the results obtained by using various decision tree algorithms.
Recently, we have proposed a new approach to estimation of the coefficients of eigenimages, which is robust against occlusion, varying background, and other types of non-Gaussian noise. In this paper we show that our ...
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Recently, we have proposed a new approach to estimation of the coefficients of eigenimages, which is robust against occlusion, varying background, and other types of non-Gaussian noise. In this paper we show that our method for estimating the coefficients can be applied to convolved and subsampled images yielding the same value of the coefficients. This enables an efficient multiresolution approach, where the values of the coefficients can directly be propagated through the scales. This property is used to extend our robust method to the problem of scaled images. We performed extensive experimental evaluations to confirm our theoretical results.
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