Outline

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

2. 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 Visual Perception Modeling

3. Comments on General Feature Statistics Visual Perception Modeling

4. 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 Visual Perception Modeling

5. Multi-resolution Sampling Visual Perception Modeling

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

7. Multi-resolution Sampling – cont. More results at http://www.ai.mit.edu/~jsd 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 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 Visual Perception Modeling

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

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

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

16. 1st (Distance: 0.05) 12th (Distance: 0.21) 6th (Distance: 0.14) Content-based Image Retrieval • Image retrieval example using spectral histogram http://www-dbv.cs.uni-bonn.de/image/mixture.tar.gz 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 Visual Perception Modeling

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

21. Texture Segmentation - continued Visual Perception Modeling

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