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Texture Analysis

Entropy, range, and standard deviation filtering; create gray-level co-occurrence matrix

Functions

entropyEntropy of grayscale image
entropyfiltLocal entropy of grayscale image
rangefiltLocal range of image
stdfiltLocal standard deviation of image
graycomatrixCreate gray-level co-occurrence matrix from image
graycopropsProperties of gray-level co-occurrence matrix

Topics

Texture Analysis

Texture analysis uses statistical measures to classify textures. It can detect the boundaries of objects that are characterized more by texture than by intensity.

Detect Regions of Texture in Images

This example shows how to detect edges and contours of objects in an image based on the texture of the objects against the background.

Texture Analysis Using the Gray-Level Co-Occurrence Matrix (GLCM)

The GLCM characterizes texture based on the number of pixel pairs with specific intensity values arranged in specific spatial relationships.

Create a Gray-Level Co-Occurrence Matrix

When you create a single GLCM, the default spatial relationship is defined as two horizontally adjacent pixels.

Specify Offset Used in GLCM Calculation

You can create multiple GLCMs with different spatial relationships between pixels to obtain additional information about textural features.

Derive Statistics from GLCM and Plot Correlation

This example shows how to create a set of GLCMs and derive statistics from them.

Texture Segmentation Using Gabor Filters

This example shows how to use texture segmentation to identify regions based on their texture.

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