Image texture analysis refers to the process of extracting meaningful information about the spatial variation of pixel intensities in an image. There are various methods used for image texture analysis. Some of them are:

  1. Gray-Level Co-occurrence Matrix (GLCM): GLCM is a statistical method that measures the spatial relationship between pairs of pixels. It computes the probability of occurrence of different pixel intensity pairs at a given spatial distance and direction. GLCM features such as contrast, energy, homogeneity, and entropy can be used to describe the texture of an image.

  2. Gabor Filters: Gabor filters are a set of bandpass filters that are specifically tuned to capture texture information in different frequency and orientation domains. They are commonly used for texture analysis as they provide localized frequency and orientation information.

  3. Laws' Texture Energy Measures: Laws' texture energy measures are a set of filters that capture different types of texture energy such as edge orientation, edge strength, and spot/localized energy. These filters are designed to mimic the response of human visual system cells to different types of textures.

  4. Local Binary Patterns (LBP): LBP is a simple yet effective texture descriptor that encodes the local structure of an image by comparing the intensity value of a central pixel with its neighboring pixels. LBP is a computationally efficient approach and is widely used in texture analysis tasks.

  5. Wavelet Transform: Wavelet transform is a multiresolution analysis technique that decomposes an image into different frequency bands. It allows capturing texture information at different scales, and the coefficients obtained at different resolutions can be used as texture features.

  6. Fractal Analysis: Fractal analysis characterizes the self-similarity and complexity of an image texture using fractal dimension and lacunarity measures. Fractal descriptors provide a different perspective on texture analysis and can capture texture patterns with scale invariance.

  These are just a few examples of the methods used for image texture analysis. The choice of method depends on the specific task, image characteristics, and the desired level of detail in texture representation.