This document discusses texture analysis in image processing. It defines texture as the spatial arrangement of color or intensities in an image that can help with image segmentation and classification. There are two main approaches to texture analysis: structural, which looks at regular patterns of texels, and statistical, which analyzes relationships between pixel intensities using methods like edge detection, co-occurrence matrices, and histograms. Statistical texture analysis captures the degrees of randomness and regularity in textures through metrics calculated from pixel intensity distributions and relationships.

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Texture definition
Identification
Approaches
Statistical
 Edge detection
 Co-occurrence measure
 Texture segmentation
 boundary based
 region based

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. Texture analysis
 Because texture has so many different
dimensions.
 no single method of texture representation
that is adequate for a variety of textures.

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
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6. Structured approach
Structural approach: a
set of texels in some regular or repeated pattern
Repeated 12 times
Repeated 12 times

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. Textures
Bark texture
wood texture

9. Different textures
Carpet
texture
fabrics

10. Stone texture
water texture

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. 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.