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 SPIE and IS&T.

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Abstract:Most consumers do not want to edit their images, either because they do not have the time, or they do not have the know how. They do want to be able to press a button that will magically make the objects captured in their photo look better. At the heart of enabling such functionality, lay image analysis models. The more we know about the objects in the photo, the better we can enhance and modify it according to human preferences. We present a necessary piece of this puzzle, breaking the image into signi¯cant segments and ¯nding important perceptual objects including skin, sky, snow and foliage. Abstract Most consumers do not want to edit their images, either because they do not have the time, or they do not have the know how. They do want to be able to press a button that will magically make the objects captured in their photo look better. At the heart of enabling such functionality, lay image analysis models. The more we know about the objects in the photo, the better we can enhance and modify it according to human preferences. We present a necessary piece of this puzzle, breaking the image into significant segments and finding important perceptual objects including skin, sky, snow and foliage.

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