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|Title:||Recovering the Orientation of Textured Surfaces in Natural Scenes (Image Processing)|
|Department / Program:||Computer Science|
|Degree Granting Institution:||University of Illinois at Urbana-Champaign|
|Abstract:||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. This thesis presents a method for recovering the orientation of textured surfaces while simultaneously identifying texture elements. Candidate texture elements are constructed from overlapping circular regions of relatively uniform gray-level. The uniform circular regions are found by convolving the image with $\nabla\sp2G$ (Laplacian-of-Gaussian) masks over a range of scales, and comparing the convolution output to that expected for a circular disk of constant gray level. True texture elements are selected from the set of candidate texture elements by finding the planar surface that best predicts the properties of the candidate texture elements. Each planar fit is evaluated by comparing the predicted texture-element areas to the actual areas of the candidate texture elements. The planar fit receiving support from the most regions in chosen as the correct interpretation. Simultaneously, those candidate texture elements that support the best plane are identified as the true texture elements. Results are shown on images of many natural textures, including rocks, leaves, waves, flowers, bark, and clouds. Textures consist of both bright and dark regions, corresponding to lit and shadowed areas, or to foreground and background. The positive-contrast and negative-contrast regions of each image are analyzed separately. The two analyses often result in slant and tilt estimates that are within ten degrees of each other; images where the discrepancy is larger have specific textural properties that cause inaccuracies in one or both of the analyses.|
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1987.
|Date Available in IDEALS:||2014-12-15|