TEXTURE

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TEXTURE

• An attribute representing the spatial arrangement
  of the gray levels of the pixels in a region.

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TEXTURE
REGULAR
STATISTICAL ISOTROPIC
STATISTICAL ANISOTROPIC
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STATISTICAL METHODS FOR TEXTURE ANALYSIS
H(g)
Single Pixel
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STATISTICAL METHODS FOR TEXTURE ANALYSIS
H (g1,g2)
SECOND ORDER STATISTICS How often do grey values
co-occur at two pixels separated by a
fixed distance and direction ..
(Di,Dj)
OF CO-OCCURRENCES
(Di,Dj)
256
256 x 256 2D matrix array where entries
are co-occurrence values
256
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Nth-order STATISTICS
It has been found that humans can discriminate
textures with different 2nd-order statistics but
are bad at discriminating 3rd order
statistics (Julesz 1981).
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2nd ORDER STATISTICS
256
256 x 256 2D matrix array where entries
are co-occurrence values
256
Since only pixel elements over short distances
are correlated (Di,Dj) is typically small
e.g., (1,0), (0,1), (1,1)
Since for 256 grey values the 2D matrix is
typically sparse, co-occurences are typically
taken over 8 grey value ranges and less (e.g.,
for 256 grey values they are grouped into 8 grey
value bins or less)
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2nd ORDER STATISTICS
grey values
2D matrix array where entries are co-occurrence
values
grey values
Symmetrize the co-occurrence matrix by adding
itself to its transpose.
T


Besides making this less sensitive to how the
image plane is coordinatized this real positive
symmetric matrix has nice rotational invariants
such as eigenvalues.
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TEXTURE MEASURES DERIVED FROM THE CO-OCCURRENCE
MATRIX
ENTROPY S S Cij logCij
INERTIA S S (i - j) Cij
2
ENERGY S S Cij
2
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SOME EXAMPLE TEXTURES TO CLASSIFY FOR A FINAL
PROJECT
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PHOTOMETRIC STEREO
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PHOTOMETRIC STEREO

• No correspondence problem as is present in
  binolcular stereo
• Measures surface orientation rather than depth.
• Is active rather than passive.