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Segmentation Using Texture

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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  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 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

  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 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

  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 1998 http://www.s2.chalmers.se/undergraduate/courses0203/ess060/PDFdocuments/ForPrinter/Notes/TextureAnalysis.pdf

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

  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”, http://www-mm.informatik.unimannheim.de/veranstaltungen/animation/multimedia/segmentation/documentation/Segmentation.pdf

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

  15. Blaž Luin Dumitru Şipoş Zoltán Kiss Kornél Kovács

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