Fractal-Based Description of Natural Scenes

1984 IEEE Transactions on Pattern Analysis and Machine Intelligence 1,783 citations

Abstract

This paper addresses the problems of 1) representing natural shapes such as mountains, trees, and clouds, and 2) computing their description from image data. To solve these problems, we must be able to relate natural surfaces to their images; this requires a good model of natural surface shapes. Fractal functions are a good choice for modeling 3-D natural surfaces because 1) many physical processes produce a fractal surface shape, 2) fractals are widely used as a graphics tool for generating natural-looking shapes, and 3) a survey of natural imagery has shown that the 3-D fractal surface model, transformed by the image formation process, furnishes an accurate description of both textured and shaded image regions. The 3-D fractal model provides a characterization of 3-D surfaces and their images for which the appropriateness of the model is verifiable. Furthermore, this characterization is stable over transformations of scale and linear transforms of intensity. The 3-D fractal model has been successfully applied to the problems of 1) texture segmentation and classification, 2) estimation of 3-D shape information, and 3) distinguishing between perceptually ``smooth'' and perceptually ``textured'' surfaces in the scene.

Keywords

FractalArtificial intelligenceFractal dimensionFractal landscapeComputer visionComputer scienceFractal compressionFractal analysisSurface (topology)Image segmentationSegmentationImage textureScale (ratio)Texture (cosmology)Image (mathematics)Image processingGeometryMathematicsGeographyMathematical analysisCartography

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Publication Info

Year
1984
Type
article
Volume
PAMI-6
Issue
6
Pages
661-674
Citations
1783
Access
Closed

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84
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Cite This

Alex Pentland (1984). Fractal-Based Description of Natural Scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence , PAMI-6 (6) , 661-674. https://doi.org/10.1109/tpami.1984.4767591

Identifiers

DOI
10.1109/tpami.1984.4767591
PMID
22499648

Data Quality

Data completeness: 81%