Texture Analysis

In many machine vision and image processing algorithms, simplifying assumptions are made about the uniformity of intensities in local image regions. However, images of real objects often do not exhibit regions of uniform intensities. For example, the image of a wooden surface is not uniform but contains variations of intensities which form certain repeated patterns called visual texture. The patterns can be the result of physical surface properties such as roughness or oriented strands which often have a tactile quality, or they could be the result of reflectance differences such as the color on a surface.

Image texture, defined as a function of the spatial variation in pixel intensities (gray values), is useful in a variety of applications and has been a subject of intense study by many researchers. One immediate application of image texture is the recognition of image regions using texture properties. For example, based on textural properties, we can identify a variety of materials such as cotton canvas, straw matting, raffia, herringbone weave, and pressed calf leather. Texture is the most important visual cue in identifying these types of homogeneous regions. This is called texture classification. The goal of texture classification then is to produce a classification map of the input image where each uniform textured region is identified with the texture class it belongs to.

We could also find the texture boundaries even if we could not classify these textured surfaces. This is then the second type of problem that texture analysis research attempts to solve -- texture segmentation. The goal of texture segmentation is to obtain the boundary map separating the differently textured regions in an image.

Texture synthesis is often used for image compression applications. It is also important in computer graphics where the goal is to render object surfaces which are as realistic looking as possible.

The shape from texture problem is one instance of a general class of vision problems known as ``shape from X.'' The goal is to extract three-dimensional surface shape from variations in textural properties in the image. The texture features (texture elements) are distorted due to the imaging process and the perspective projection which provide information about surface orientation and shape.

Related Publications

  1. M. Tuceryan and A. K. Jain, ``Texture Analysis,'' In The Handbook of Pattern Recognition and Computer Vision (2nd Edition), by C. H. Chen, L. F. Pau, P. S. P. Wang (eds.), pp. 207-248, World Scientific Publishing Co., 1998. (Abstract) (Book Chapter)
  2. M. Tuceryan, ``Moment Based Texture Segmentation.'' in Pattern Recognition Letters, vol. 15, pp. 659-668, July 1994. (Abstract)
  3. M. Tuceryan and A. K. Jain, ``Texture Segmentation Using Voronoi Polygons,'' IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. PAMI-12, pp. 211-216, February, 1990. (Abstract)