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This paper presents paper retrieval using the specific paper features chain and laid lines. Paper features are detected in digitized paper images and they are represented such that they could be used for retrieval. Optimal retrieval performance is achieved by means of a trainable similarity measure for a given set of paper features. By means of these methods a retrieval system is developed that art experts could use real-time in order to speed up their paper research. [ABSTRACT FROM AUTHOR]

The Parzen-window approach is a well-known technique for estimating probability density functions. This paper introduces a modulated Parzen-windows approach. This approach uses kernels at equidistant samples to obtain a probability density function more efficiently. Experiments on both artificial and real data show that the modulated Parzen-windows approach is more efficient in probability density function estimation, without costly preprocessing or severe loss of accuracy. [ABSTRACT FROM AUTHOR]