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 various 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.
|Entropy of grayscale image|
|Local entropy of grayscale image|
|Local range of image|
|Local standard deviation of image|
Texture analysis uses statistical measures to classify textures. It can detect the boundaries of objects that are characterized more by texture than by intensity.
This example shows how to detect edges and contours of objects in an image based on the texture of the objects against the background.
The GLCM characterizes texture based on the number of pixel pairs with specific intensity values arranged in specific spatial relationships.
When you create a single GLCM, the default spatial relationship is defined as two horizontally adjacent pixels.
You can create multiple GLCMs with different spatial relationships between pixels to obtain additional information about textural features.
This example shows how to create a set of GLCMs and derive statistics from them.
This example shows how to use texture segmentation to identify regions based on their texture.