# TensorFlow 101C. Image Texture

soma without prescription COD This Note is for image texture explanation:

- Reference

https://courses.cs.washington.edu/courses/cse576/book/ch7.pdf (computer vision)

- Why Texture

Texture gives us information about the spatial arrangement of the colors or intensities in an image. Why?

The answer is the histogram can’t fully represent/classify images. All images below are half white and half black. However, the images are different.

- How to recognize texture

- Structural approach: Texture is a set of primitive texels in some regular or repeated relationship.
- Statistical approach: Texture is a quantitative measure of the arrangement of intensities in a region.

- Statistical method

- Co-occurrence Matrices and Feature

- Basic concept

- Explanation in data:

In the matrix of C(0,1), the first row is 4, 0, 2. (Which measure the occurrence of the values of two adjacent cells in the same row)

[0,0]=4 means: In the two adjacent cells (in the same row) of Image I, the # of of occurrence of (the left cell =0 and the right cell=0) is 4, as shown the red rectangles in image below.

[0,1]=0 means: in the two adjacent cells (in the same row) of Image I, the # of of occurrence of (the left cell =0 and the right cell=1) is 0.

[0,2]=2 means: in the two adjacent cells (in the same row) of Image I, the # of of occurrence of (the left cell =0 and the right cell=2) is 2.

- Probability of the occurrence:

- Co-occurrence matrices capture properties of a texture, but they are not directly useful

For further analysis, such as comparing two textures. Instead, numeric features are computed from the co-occurrence matrix that can be used to represent the texture more compactly. The following are standard features derivable from a normalized co-occurrence matrix.

- Laws’ Texture Energy Measure

Another approach to generating texture features is to use local masks to detect various Types of texture. Laws developed a texture-energy approach that measures the amount of variation within a fixed-size window

Figure 7.8 shows the segmentation of several images using

The Laws texture energy measures and a clustering algorithm to group pixels into regions

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