segmentation using texture l.
Skip this Video
Loading SlideShow in 5 Seconds..
Segmentation Using Texture PowerPoint Presentation
Download Presentation
Segmentation Using Texture

Loading in 2 Seconds...

play fullscreen
1 / 15

Segmentation Using Texture - PowerPoint PPT Presentation

  • Uploaded on

Segmentation Using Texture. Project Description. Input: satellite image and a texture Task: segmentation of the image based on the texture Output: labeled image. What Is a Texture ?. There are many definitions of the word texture:

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about 'Segmentation Using Texture' - adriano

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
project description
Project Description
  • Input: satellite image and a texture
  • Task: segmentation of the image based on the texture
  • Output: labeled image
what is a texture
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)
  • Histogram matching
  • Law’s texture measure
  • Run-length matrices
histogram matching algorithm i
Histogram Matching Algorithm I

Short description:

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.

The texture we are searching (the template)

Window at step k

(the sample)

Window at step k+1

histogram matching algorithm ii
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 fand the one of the overlapped area g:


Histogram Transformation in Image Processing and Its Applications by Attila Kuba, University of Szeged

histogram matching algorithm iii
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


You can choose an algorithm for the search (we have more than one )

You should wait (but not too long) for the resulting image

run length algorithm i
Run-length Algorithm I

City – rough grayscale variations – short runs

= P

Grass – smooth grayscale variations – long runs

= P

run length algorithm ii
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

law s texture measure i
Law’s Texture Measure I

First step:



Measure energy

Horizontal kernel

Measure energy

Law’s energy


Original image


Chantler, Michael J, “The effect of variation in illuminant direction on texture classification”, pp 90-134,

law s texture measure ii
Law’s Texture Measure II

Second step:



Binary dilation


Law’s energy


Segmented image


Krabbe, Susanne, “Still Image Segmentation”,

law s texture measure iii
Law’s Texture Measure III

Original image

Output image


Blaž Luin

Dumitru Şipoş

Zoltán Kiss

Kornél Kovács