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Fractal-Based Description of Natural Scenes

Pentland, Alex P.·1984·IEEE Transactions on Pattern Analysis and Machine Intelligence

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.

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Metadata

Authors
Pentland, A. P.
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Published
1984-11
Volume
PAMI-6
Issue
6
Pages
661-674
ISSN
0162-8828 (ISSN-L)
OpenAlex
W2158270878
Cited by
1,800
References
0
FWCI
18.60
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Fractal-Based Description of Natural Scenes. https://app.cochat.ai/papers/10.1109/tpami.1984.4767591
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Pentland, A. P. (1984). Fractal-Based Description of Natural Scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.1984.4767591

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.