IEEE Transactions on Pattern Analysis and Machine Intelligence · 1984 · 791 citations · 9 references
ComputingComputer ScienceComputer VisionDigital ImagingImage InterpretationFractal GeometryImage ClassificationImage TextureSurface TextureTexture AnalysisTexture EvaluationFractal Texture AnalysisImage Texture AnalysisMulti-scale Texture AnalysisResolution AnalysisResolutionsTexture ClassificationTexture ModelingTexture Recognition
Texture classification often depends on how image properties vary with resolution, noting that coarser scales reduce fine‑detail area and that relationships to negatives and directional cues are also considered. The method measures gray‑level surface area at multiple resolutions, derives fractal characteristics from the rate of area decrease, and uses these features to compare and classify textures.
Textures are classified based on the change in their properties with changing resolution. The area of the gray level surface is measured at serveral resolutions. This area decreases at coarser resolutions since fine details that contribute to the area disappear. Fractal properties of the picture are computed from the rate of this decrease in area, and are used for texture comparison and classification. The relation of a texture picture to its negative, and directional properties, are also discussed.
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The Fractal Geometry of Nature
Benoît B. Mandelbrot, Paul Longley · 1982 · 5.5K citations
Textures: A Photographic Album for Artists and Designers
Irwin Hersey, Phil Brodatz · Leonardo · 1968 · 2.7K citations
<i>Fractals: Form, Chance, and Dimension</i>
B. B. Mandelbrot, Michael Aizenman · Physics Today · 1979 · 2.2K citations