Paper
8 June 2001 Recognition methods for 3D textured surfaces
Oana G. Cula, Kristin J. Dana
Author Affiliations +
Proceedings Volume 4299, Human Vision and Electronic Imaging VI; (2001) https://doi.org/10.1117/12.429492
Event: Photonics West 2001 - Electronic Imaging, 2001, San Jose, CA, United States
Abstract
Texture as a surface representation is the subject of a wide body of computer vision and computer graphics literature. While texture is always associated with a form of repetition in the image, the repeating quantity may vary. The texture may be a color or albedo variation as in a checkerboard, a paisley print or zebra stripes. Very often in real-world scenes, texture is instead due to a surface height variation, e.g. pebbles, gravel, foliage and any rough surface. Such surfaces are referred to here as 3D textured surfaces. Standard texture recognition algorithms are not appropriate for 3D textured surfaces because the appearance of these surfaces changes in a complex manner with viewing direction and illumination direction. Recent methods have been developed for recognition of 3D textured surfaces using a database of surfaces observed under varied imaging parameters. One of these methods is based on 3D textons obtained using K-means clustering of multiscale feature vectors. Another method uses eigen-analysis originally developed for appearance-based object recognition. In this work we develop a hybrid approach that employs both feature grouping and dimensionality reduction. The method is tested using the Columbia-Utrecht texture database and provides excellent recognition rates. The method is compared with existing recognition methods for 3D textured surfaces. A direct comparison is facilitated by empirical recognition rates from the same texture data set. The current method has key advantages over existing methods including requiring less prior information on both the training and novel images.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Oana G. Cula and Kristin J. Dana "Recognition methods for 3D textured surfaces", Proc. SPIE 4299, Human Vision and Electronic Imaging VI, (8 June 2001); https://doi.org/10.1117/12.429492
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Cited by 42 scholarly publications.
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KEYWORDS
3D image processing

Databases

Detection and tracking algorithms

Principal component analysis

Image segmentation

Object recognition

Monte Carlo methods

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