segmentation using texture l.
Download
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


  • 132 Views
  • 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:

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
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)
algorithms
Algorithms
  • 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:

FOR MORE INFO...

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

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

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

FOR MORE INFO...

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

law s texture measure i
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 90-134, http://www.cee.hw.ac.uk/~mjc/texture/mjc-phd/

law s texture measure ii
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”, http://www-mm.informatik.unimannheim.de/veranstaltungen/animation/multimedia/segmentation/documentation/Segmentation.pdf

law s texture measure iii
Law’s Texture Measure III

Original image

Output image

slide15

Blaž Luin

Dumitru Şipoş

Zoltán Kiss

Kornél Kovács