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# 1st Day

1st Day. Lecture 1: Intro. Goal of Vision. To understand and interpret the image. Images consist of many different patterns – grass, faces, crowds. Vision is easy for Humans. Because a very large part of our brain does vision. Half the Cortex does Vision.

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## 1st Day

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### Presentation Transcript

1. 1st Day Lecture 1: Intro

2. Goal of Vision • To understand and interpret the image. • Images consist of many different patterns – grass, faces, crowds.

3. Vision is easy for Humans • Because a very large part of our brain does vision. • Half the Cortex does Vision. • (Much more than does mathematics or computer science or other ‘high level’ tasks.)

4. Vision is very difficult • Because images are complex and ambiguous. • Left panels (top) shows two bicycles • Left panels (bottom) show intensity plots I(i,j).

5. Vision as Decoding • Vision is an Inverse Inference Problem

6. Bayes Theorem. • Bayes (left) uses prior knowledge to resolve ambiguity (right).

7. Lecture 2: Images and Filters

8. Statistics of Image Gradients • Left: Everywhere. Right: On and Off Edges.

9. Lecture 3: Edges

10. Edges: Sowerby Dataset • Top: Example of Images and groundtruth • Bottom: Canny (left), Statistical method (center). • Loglikelihood ratios (right)

11. Edges: Combing Scales • Results: Chernoff performance measures (risk). • Triangles (grad I). Cross (Harris-Nitzberg).

12. Edges: Combining Directions • Results using combinations of oriented filters (Gabors).

13. Edge Detection is Hard • Distributions overlap: ROC curves.

14. Lecture 4: Weak Smoothness

15. Steepest Descent and Variations • Steepest Descent and Discrete Iterative.

16. Lecture 5 (Manhattan World)

17. Manhattan world • The world has Manhattan structure. • And humans make mistakes when it does not. • Identical twins in Ames room.

18. Geometry • Projection and Vanishing Points

19. Manhattan Results • Good results for Indoor Images

20. Manhattan Images • Results for City Scenes

21. Manhattan Countryside • Some images in the country also have Manhattan structure.

22. Non-Manhattan Images • Some images are not Manhattan – verify by model selection: compare P_{man} to P_{null}

23. Lecture 6 • Image Classification – independent.

24. Sowerby and San Francisco • Examples.

25. Results: • Color only (left). Texture only (right). Sowerby only.

26. Results: • Color and Texture: Sowery (left), San Francisco (Right).

27. Examples. • Sowerby:

28. Examples • San Francisco

29. Medical Applications • Apply similar ideas to medical images. • Tumor detection.

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