1. Outline Announcement Texture modeling - continued Some remarks
Applications of texture modeling

2. Visual Perception Modeling
Announcement
The presentation schedule is on the web
Now you should have almost completed your project
You need to take it very seriously in order to get a good grade for this class
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Visual Perception Modeling

3. Comments on General Feature Statistics
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Visual Perception Modeling

4. Visual Perception Modeling
Joint Statistics
FRAME and Julesz ensemble models use marginal distributions of feature statistics
It might be useful to consider joint statistics for more powerful models
Joint statistics will be more precise because filter responses are not independent of each other
However, this model should include all the images of the same texture type; an over-constrained model will include only the original image
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Visual Perception Modeling

5. Multi-resolution Sampling
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Visual Perception Modeling

6. Multi-resolution Sampling – cont.
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Visual Perception Modeling

7. Multi-resolution Sampling – cont.
More results at
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Visual Perception Modeling

8. Applications of Texture Models
Inspection
There has been a limited number of texture processing for automated inspection problems
Detection of defects of textiles
Detection of defects of lumber wood automatically
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Visual Perception Modeling

9. Applications of Texture Models – cont.
Medical image analysis
Image analysis techniques have played an important role in several medical applications
Texture features are used to distinguish normal tissues from abnormal tissues
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Visual Perception Modeling

10. Applications of Texture Models – cont.
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Visual Perception Modeling

11. Applications of Texture Models – cont.
Document processing
Document image analysis and character recognition
Applications ranging from postal address recognition to interpretation of maps
Based on the characteristics of printed documents
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Visual Perception Modeling

12. Applications of Texture Models – cont.
Remote sensing
Texture analysis has been used extensively to classify remotely sensed images
Land use classification
Automated image analysis
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Visual Perception Modeling

13. Applications of Texture Models – cont.
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Visual Perception Modeling

14. Applications of Texture Models – cont.
Content-based image retrieval
Try to retrieve images that are meaningful in certain sense
For example, to find all the images that like the examples
To find all the images that contain a horse
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Visual Perception Modeling

15. Applications of Texture Models – cont.
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Visual Perception Modeling

16. Content-based Image Retrieval
Image retrieval example using spectral histogram
1st (Distance: 0.05)
6th (Distance: 0.14)
12th (Distance: 0.21)
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Visual Perception Modeling

17. Applications of Texture Models – cont.
Texture segmentation
Image segmentation is to partition an image into roughly homogenous regions
Segmentation is more difficult than classification
Feature statistics not known
Boundaries to be localized
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Visual Perception Modeling

18. Texture Segmentation - continued
Identify feature statistics using spatial constraints
Pixels within a homogenous region have similar spectral histogram
Input image
Initial regions
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Visual Perception Modeling

19. Texture Segmentation - continued
Classify each pixel using the extracted feature statistics
Error with respect to the ground truth is 6.55 %
Initial classification result
Error from the ground truth
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Visual Perception Modeling

20. Texture Segmentation - continued
Boundary localization using structural information
The segmentation error is 0.95 %
Segmentation result
Error from the ground truth
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Visual Perception Modeling

21. Texture Segmentation - continued
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Visual Perception Modeling

22. Texture Segmentation - continued
Input image
Result superimposed
Canny edge map
Segmentation result
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Visual Perception Modeling