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Mastering Image Vision: Insights, Techniques, and Applications

Delve into the intricacies of image interpretation, from understanding patterns to edge detection and classification. Explore the challenges and successes of visual inference through lectures on filters, edges, smoothness, Manhattan structure, and image classification, with practical applications in medical imaging. Enhance your knowledge of visual processing and pattern recognition in this comprehensive course.

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Mastering Image Vision: Insights, Techniques, and Applications

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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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