1. Segmentation Using Texture

2. Project Description Input: satellite image and a texture
Task: segmentation of the image based on the texture
Output: labeled image

3. What Is a Texture ? There are many definitions of the word texture:
Describes something that has a surface that is not smooth but has a raised pattern on it (from Cambridge advanced learner's dictionary)
A measure of the variation of the intensity of a surface, quantifying properties such as smoothness, coarseness and regularity (from FOLDOC - computing dictionary)

4. Algorithms Histogram matching Law’s texture measure
Run-length matrices

5. Histogram Matching Algorithm I
Short description:
The texture we are searching (the template)
Window at step k
(the sample)
Window at step k+1
The basic idea is to compute the histogram of the template, and then sweep a window over the image, compute the histogram of the window and do a correlation between the histograms.

6. Histogram Matching Algorithm II
Histogram equalization (HE) of the image:
Calculate the histogram of the texture
Overlap the image by the texture at each possible position and calculate correlation of the histogram of the texture f and the one of the overlapped area g:
FOR MORE INFO...
Histogram Transformation in Image Processing and Its Applications by Attila Kuba, University of Szeged

7. Histogram Matching Algorithm III
Thresholding of the correlation map:
High correlated values are set to 1
Low correlated values are set to 0
This yields a binary image BI
Median filter to eliminate the holes on BI
Border := BI – erosion(BI)
Put the border on the original image
OBSERVATION...
You can choose an algorithm for the search (we have more than one )
You should wait (but not too long) for the resulting image

8. Histogram Matching Algorithm IV
Zoomed texture

9. Histogram Matching Algorithm V
Zoomed texture

10. Run-length Algorithm I
City – rough grayscale variations – short runs
= P
Grass – smooth grayscale variations – long runs
= P

11. Run-length Algorithm II
Second step:
Calculate short run emphasis
Calculate long run emphasis
Calculate gray level nonuniformity
Find closest matches
FOR MORE INFO...
Tang, Xiaoou, “Texture Information in Run-Length Matrices”, IEEE transactions on image processing, vol. 7, no 11, november

12. Law’s Texture Measure I
First step:
Vertical
kernel
Measure energy 
Horizontal kernel
Measure energy
Law’s energy
matrix
Original image
FOR MORE INFO...
Chantler, Michael J, “The effect of variation in illuminant direction on texture classification”, pp ,

13. Law’s Texture Measure II
Second step:
Grayscale
dilation
Binary dilation
Thresholding
Law’s energy
matrix
Segmented image
FOR MORE INFO...
Krabbe, Susanne, “Still Image Segmentation”,

14. Law’s Texture Measure III
Original image
Output image

15. Blaž Luin
Dumitru Şipoş
Zoltán Kiss
Kornél Kovács