Image Processing Toolbox User's Guide

Analyzing the Texture of an Image

The toolbox supports a set of functions that you can use for texture analysis. Texture analysis refers to the characterization of regions in an image by their texture content. Texture analysis attempts to quantify intuitive qualities described by terms such as rough, smooth, silky, or bumpy as a function of the spatial variation in pixel intensities. In this sense, the roughness or bumpiness refers to variations in the intensity values, or gray levels.

Texture analysis is used in a variety of applications, including remote sensing, automated inspection, and medical image processing. Texture analysis can be used to find the texture boundaries, called texture segmentation. Texture analysis can be helpful when objects in an image are more characterized by their texture than by intensity, and traditional thresholding techniques cannot be used effectively.

The toolbox provides two types of texture functions:

• Texture filter functions -- These functions use standard statistical measures to characterize the local texture of an image. See Using Texture Filter Functions for more information.
• Gray-level co-occurrence matrix -- These functions characterize the texture of an image by calculating how often pairs of pixel with specific values and in a specified spatial relationship occur in an image and then extracting statistical measures from this matrix. See Using a Gray-Level Co-Occurrence Matrix (GLCM) for more information

Using Quadtree Decomposition

Using Texture Filter Functions

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