Shape from Texture: Integrating Texture-Element Extraction and Surface Estimation

Dorothea Blostein, Narendra Ahuja

Research output: Contribution to journalArticlepeer-review


A perspective view of a slanted textured surface shows systematic changes in the density, area, and aspect-ratio of texture elements. These apparent changes in texture element properties can be analyzed to recover information about the physical layout of the scene. However, in practice it is difficult to identify texture elements, especially in images where the texture elements are partially occluded or are themselves textured at a finer scale. To solve this problem, it is necessary to integrate the extraction of texture elements with the recognition of scene layout. We present a method for identifying texture elements while simultaneously recovering the orientation of textured surfaces. A multiscale region detector, based on measurements in a V2G (Laplacian-of-Gaussian) scale-space, is used to construct a set of candidate texture elements. True texture elements are selected from the set of candidate texture elements by finding the planar surface that best predicts the observed areas of the candidate texture elements. Results are shown for a variety of natural textures, including waves, flowers, rocks, clouds, and dirt clods.

Original languageEnglish (US)
Pages (from-to)1233-1251
Number of pages19
JournalIEEE transactions on pattern analysis and machine intelligence
Issue number12
StatePublished - Dec 1989

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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