1. On Morphological Color Texture Characterization
Erchan Aptoula and Sébastien Lefèvre
Image Sciences, Computer Sciences and Remote Sensing Laboratory
Louis Pasteur University
Strasbourg, France
October 12, 2007
ISMM

2. Contents Morphological tools for texture analysis
Granulometry & covariance
A combination of SE size-direction-distance
Implementation on color images
Application results
MB ve doku çözümleme
İki ana işlecin özellikleri
Boyut-yön-uzaklık karışımı
Renkli görüntülere uygulaması
Ve deney sonuçları
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3. Morphological texture description
Texture characteristics: regularity (periodicity),
directionality, complexity, overall color and color purity
Burayı olduğugibi okuyoruz.
A rich variety of tools: granulometry, morphological covariance-variogram, orientation maps, etc
Main advantage of morphological approaches: their inherent capacity to exploit spatial pixel relations
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4. Granulometry Standard granulometry of an image f :
Extracts information on the granularity of its input
Has several extensions: attribute based, multivariate, spatial, etc
Evet nedir granulometry?
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5. Morphological covariance
Morphological covariance of an image f :
P2,v: a pair of points separated by a vector v
Extracts information on the regularity, directionality and coarseness of its input
Morphological equivalent of the autocorrelation operator
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6. Covariance + granulometry = ?
granularity
Covariance
regularity
directionality
coarseness
They extract complementary information, how should they be combined, by concatenation, ... or?
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7. Covariance + granulometry = ?
Employ 3 structuring element variables: size, direction and distance.
Size granularity
Distance regularity
Direction directionality
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8. Covariance + “granulometry”
Pλ,v: a pair of SEs of size λ, separated by a vector v
However: SE is not convex pseudo granulometry
Aşınım yerine açınım düzenli biçimde daha iyi sonuç verdiğinden yerine o yerleştirildi..ayrıca süzgeç ilkesine sadık kalmak için de. Ancak SE convx olmadığından zaten pseudo granulometry oluyor sonuç.
Strongly ordered texture
Disordered texture
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9. Extending to color images
Requirements:
A suitable color space
A color ordering scheme (preferably total), to impose a lattice structure R G B
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10. Color space choice Color space choice : perceptual, polar, etc...
Polar color spaces : (+) intuitive components
(-) manipulation of hue
(-) multiple implementations
Hangi uzayı neden seçiyoruz. Ayrıca bu aşamada neden olduğu sorunlardan kaçınmak için renközünü de kullanmamayı seçiyoruz.
Luminance
Saturation
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11. Color ordering
Luminance: contains the majority of variational information
Color: auxiliary component
For which levels of luminance does color become more important ?
2. How should the balance between luminance and “color” use be determined ?
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12. Color ordering
For which levels of luminance does color become more important ?
Luminance is roughly separated into three regions. Main problem: saturation luminance relation. a b c
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13. Color ordering
2. How should the balance between luminance and “color” use be determined ?
Image or vector specific configurations are better suited for intra-image applications
Here, an image database specific approach is used, by means of genetic optimization:
Genelde görüntüye özel veya yöneye özel oluyor, ancak bu durum intra-image uygulamalar için uygun, örneğin bozukluk giderme gibi. Burada inter-image olduğu için görüntü tabanına özel birşey istiyoruz.
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14. Application
Outex13 texture database: 1360 images (128 x 128) of 68 colour textures
Four directions (0°,45°,90°,135°), 15 different SE sizes
(k:1 to 30, 2k+1) and 20 distances
Results in a feature of size 20x4x15, which was reduced to 20x4x2 by PCA
kNN classifier (k=1) and the
Euclidean distance
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15. Classification accuracies
Features
Grayscale
Color
Optimized Color
Granulometry
Covariance
Concatenated
Combined
67.53
73.82
77.75
83.53
68.78
76.92
79.93
85.49
72.03
80.46
83.74
88.13
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16. Conclusion and perspectives
A way of combining the complementary information provided by granulometry and covariance
However, it leads to a pseudo granulometry
Genetic optimization aids in exploiting color
Shape variations, as well as the role of hue remain to be investigated
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17. Thank you for your attention
E. Aptoula and S. Lefèvre