This paper presents two novel ways of extending the local binary pattern (LBP) texture analysis operator to multiple scales. First, large-scale texture patterns are detected by combining exponentially growing circular neighborhoods with Gaussian low-pass filtering. Second, cellular automata are proposed as a way of compactly encoding arbitrarily large circular neighborhoods. The performance of the extensions is evaluated in classifying natural textures from the Outex database. © Springer-Verlag 2003.
CITATION STYLE
Mäenpää, T., & Pietikäinen, M. (2003). Multi-scale binary patterns for texture analysis. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2749, 885–892. https://doi.org/10.1007/3-540-45103-x_117
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