Texture in image processing

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its about image procesing term texture

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Texture in image processing

  1. 1. re tu x e T Presented by :-anna
  2. 2. ad Ro     ap m Texture definition Identification Approaches Statistical  Edge detection  Co-occurrence measure  Texture segmentation  boundary based  region based
  3. 3. Definition  An image texture is a set of metrics calculated in image processing designed to quantify the perceived texture of an image  Image Texture gives us information about the spatial arrangement of color or intensities in an image or selected region of an image.  texture can be defined as an entity consisting of mutually related pixels and group of pixels.
  4. 4. Texture analysis  Because texture has so many different dimensions.  no single method of texture representation that is adequate for a variety of textures.
  5. 5. Why we used texture ?  Image textures can be artificially created or found in natural scenes captured in an image  Used to help in segmentation  classification of image Analyze texture in CG Analyze texture in CG r St ctu u re ap d a ro p ch Sta t ist i ca l ap p roa c h
  6. 6. Structured approach Structural approach: a set of texels in some regular or repeated pattern Repeated 12 times Repeated 12 times
  7. 7. Statistical approach  Texture Is a spatial property.  A simple onedimensional Histogram Is not useful in characterizing texture Example Example ((an image in which pixels an image in which pixels Alternate From black to Alternate From black to white in white in A checkerboard fashion will A checkerboard fashion will have have The same histogram as an The same histogram as an image in which the top half is image in which the top half is black and the bottom half is black and the bottom half is white). white).
  8. 8. Textures Bark texture wood texture
  9. 9. Different textures Carpet texture fabrics
  10. 10. Stone texture water texture
  11. 11.  In fact, there are many ways in which intensity might vary, but if the variation does not have sufficient uniformity, the texture may not be characterized sufficiently close to permit recognition or segmentation.  Thus, the degrees of randomness and of regularity will have to be measured and compared when charactering a texture.  Often, textures are derived from tiny objects or components that are themselves similar, but that are placed together in ways ranging from purely random to purely regular, such as bricks in a wall, or grains of sand, etc.
  12. 12. Statistical approach Co occurrence matrix  The graylevel co-occurrence matrix approach is based on studies of the statistics of pixel intensity distributions.  The co-occurrence matrices express the relative frequencies (or probabilities) P(i, j | d,θ) with which two pixels having relative polar coordinates (d,θ) appear with intensities I, j.  The co-occurrence matrices provide raw numerical data on the texture, although this data must be condensed to relatively few numbers before it can be used to classify the texture.

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