Juha Kyllönen

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This paper presents the current status of a new initiative aimed at developing a versatile framework and image database for empirical evaluation of texture analysis algorithms. The proposed Outex framework contains a large collection of surface textures captured under different conditions, which facilitates construction of a wide range of texture analysis(More)
The goal of this research was to find out if the performance of color based wood inspection systems could be improved by combining color and texture information. T h ~ s paper describes a wood surface inspection method that combines color percentile features with texture features based on simple spatial operators. The proposed method is tested with images(More)
In this paper, we propose to use learning vector quantization for the efficient partitioning of a cooccurrence space. A simple codebook is trained to map the multidimensional cooccurrence space into a 1-dimensional cooccurrence histogram. In the classification phase a nonparametric log-likelihood statistic is employed for comparing sample and prototype(More)
This paper conducts an empirical evaluation of the MPEG7 texture descriptors and the LBP (Local Binary Pattern) operator. The experiment involves 319 textures from the Outex texture database, which makes it one of the largest experiments found in the literature, if not the largest, in terms of the number of texture classes. The descriptors are evaluated(More)
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