Segmentation Using Texture

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# Segmentation Using Texture - PowerPoint PPT Presentation

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:

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## Segmentation Using Texture

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### 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:
• 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
• Histogram matching
• Law’s texture measure
• Run-length matrices
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 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
• 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

City – rough grayscale variations – short runs

= P

Grass – smooth grayscale variations – long runs

= P

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

Law’s Texture Measure I

First step:

Vertical

kernel

Measure energy

Horizontal kernel

Measure energy

Law’s energy

matrix

Original image

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

Second step:

Grayscale

dilation

Binary dilation

Thresholding

Law’s energy

matrix

Segmented image

Krabbe, Susanne, “Still Image Segmentation”, http://www-mm.informatik.unimannheim.de/veranstaltungen/animation/multimedia/segmentation/documentation/Segmentation.pdf

Law’s Texture Measure III

Original image

Output image

Blaž Luin

Dumitru Şipoş

Zoltán Kiss

Kornél Kovács