1. Outline
Texture modeling - continued
Filtering-based approaches

2. Visual Perception Modeling
Texture Modeling
The structures of images
The structures in images are due to the inter-pixel relationships
The key issue is how to characterize the relationships
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Visual Perception Modeling

3. Representing Textures through Filtering
Convolving an image with a linear filter yields a representation of the image on a different basis
The advantage of the transformation is that the process makes the local structure of the image “clear”
A filter can be viewed as a template
There is a strong response when the local image pattern looks similar to the filter kernel and a weak response when it does not
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Visual Perception Modeling

4. Representing Textures through Filtering – cont.
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Visual Perception Modeling

5. Representing Textures through Filtering – cont.
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Visual Perception Modeling

6. Representing Textures through Filtering – cont.
Gabor filters
Gabor filters have been widely in texture modeling
Mathematically, Gabor filters are optimal in the sense of local joint spatial/frequency representation
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Visual Perception Modeling

7. Representing Textures through Filtering – cont.
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Visual Perception Modeling

8. Representing Textures through Filtering – cont.
Problems with filtering-based approaches
The filter response itself does not give rise to a representation or a model for textures
Even for homogenous textures, the filter responses are not homogenous
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Visual Perception Modeling

9. Representing Textures through Filtering – cont.
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Visual Perception Modeling

10. Representing Textures through Filtering – cont.
Non-linear smoothing
In order to derive a more homogenous and meaningful feature for textures, the filter responses are then passed through a non-linear stage
The hope is that smoothed filter response will be relative homogenous within a texture region
This can be used for texture classification, texture boundary detection, and texture discrimination
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Visual Perception Modeling

11. Texture Classification Based on Filtering
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Visual Perception Modeling

12. Texture Classification Based on Filtering – cont.
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13. Texture Classification Based on Filtering – cont.
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14. Texture Classification Based on Filtering – cont.
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15. Histograms of Filter Responses as Texture Models
Histograms as texture features
For homogenous textures, histograms should not change very much
In other words, texture images with similar histograms of filter responses should look similar
Heeger and Bergen proposed a texture synthesis algorithm based on this observation
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Visual Perception Modeling

16. Histograms of Filter Responses as Texture Models – cont.
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17. Histograms of Filter Responses as Texture Models – cont.
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18. Histograms of Filter Responses as Texture Models – cont.
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19. Histograms of Filter Responses as Texture Models – cont.
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20. Histograms of Filter Responses as Texture Models – cont.
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21. Histograms of Filter Responses as Texture Models – cont.
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22. Histograms of Filter Responses as Texture Models – cont.
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23. Non-Parametric Sampling
When we need to decide a pixel value, we calculate the conditional probability given the pixel values in the surrounding neighborhood
This is done by finding the similar surrounding neighborhood in the given texture
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Visual Perception Modeling

24. Non-Parametric Sampling – cont.
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25. Non-Parametric Sampling – cont.
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26. Non-Parametric Sampling – cont.
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Visual Perception Modeling