Detection of textured areas in natural images using an indicator based on component counts

Ruth Bergman; Hila Nachlieli; Gitit Ruckenstein

doi:10.1117/1.2981836

1 October 2008 Detection of textured areas in natural images using an indicator based on component counts

Ruth Bergman, Hila Nachlieli, Gitit Ruckenstein

Author Affiliations +

Journal of Electronic Imaging, Vol. 17, Issue 4, 043003 (October 2008). https://doi.org/10.1117/1.2981836

Abstract

An algorithm is presented for the detection of textured areas in natural images. Texture detection has potential application to image enhancement, tone correction, defect detection, content classification, and image segmentation. For example, texture detection may be useful for object detection when combined with color models and other descriptors. Sky, e.g., is generally smooth, and foliage is textured. The texture detector presented here is based on the intuition that texture in a natural image is comprised of many components. The measure we develop examines the structure of local regions of the image. This structural approach enables us to detect both structured and unstructured textures at many scales. Furthermore, it distinguishes between edges and texture, and also between texture and noise. Automatic detection results are shown to match human classification of corresponding image areas.

©(2008) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Ruth Bergman, Hila Nachlieli, and Gitit Ruckenstein "Detection of textured areas in natural images using an indicator based on component counts," Journal of Electronic Imaging 17(4), 043003 (1 October 2008). https://doi.org/10.1117/1.2981836

Published: 1 October 2008

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