asd

1. 1
Segmentation Using Texture

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

3. 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. 4
Algorithms
 Histogram matching
 Law’s texture measure
 Run-length matrices

5. 5
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.
Histogram Matching Algorithm I
The texture we are searching
(the template)
Window at step k
(the sample)
Window at step k+1
Short description:

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Histogram Matching Algorithm II
i. Histogram equalization (HE) of the image:
ii. Calculate the histogram of the texture
iii. 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
 


x y
n
y
m
x
g
y
x
f
n
m
R )
,
(
)
,
(
)
,
(

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

8. 8
Histogram Matching Algorithm IV
Zoomed texture

9. 9
Histogram Matching Algorithm V
Zoomed texture

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Run-length Algorithm I
3
3
3
3
3
2
2
2
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2
2
2
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3
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2
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2
City – rough grayscale variations – short runs
Grass – smooth grayscale variations – long runs
5
0
0
2
2
5
8
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
0
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0
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0
0
0
0
6
= P
= P

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Run-length Algorithm II
Second step:
Calculate short run emphasis
Calculate long run emphasis
Calculate gray level nonuniformity
Find closest matches
Tang, Xiaoou, “Texture Information in Run-Length Matrices”, IEEE transactions on image
processing, vol. 7, no 11, november 1998
http://www.s2.chalmers.se/undergraduate/courses0203/ess060/PDFdocuments/ForPrinter/
Notes/TextureAnalysis.pdf
FOR MORE INFO...

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Law’s Texture Measure I
First step:
Original image
Vertical
kernel
Horizontal
kernel
Measure
energy
Measure
energy

Law’s energy
matrix
Chantler, Michael J, “The effect of variation in illuminant direction on texture
classification”, pp 90-134, http://www.cee.hw.ac.uk/~mjc/texture/mjc-phd/
FOR MORE INFO...

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Law’s Texture Measure II
Second step:
Law’s energy
matrix
Grayscale
dilation Thresholding
Binary
dilation
Segmented image
Krabbe, Susanne, “Still Image Segmentation”, http://www-
mm.informatik.unimannheim.de/veranstaltungen/animation/multimedia/segmentation/documentation/Seg
mentation.pdf
FOR MORE INFO...

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

15. 15
Kornél Kovács
Blaž Luin
Zoltán Kiss
Dumitru Şipoş