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Computer Vision: Reflectance Analysis for Image Understanding


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Computer Vision: Reflectance Analysis for Image Understanding

  1. 1. Michael Oren’s Thesis Damian Gordon
  2. 2. Reflectance Analysis for Image Understanding
  3. 3. How do we model these surfaces ??????????
  4. 7. Reflectance Mechanisms <ul><li>Diffuse Reflection (Body Reflection) </li></ul><ul><li>Lamertian Model </li></ul><ul><li>Specular Reflection </li></ul><ul><li>Specular Spike (smooth) </li></ul><ul><li>Specular Lobe (rough) </li></ul>
  5. 8. Reflectance Components
  6. 9. Diffuse Reflectance
  7. 10. Lambertian Model <ul><li>Essentially, surface brightness is independent of viewer direction and is determined solely by enery flux of the incident light. </li></ul>
  8. 11. Surface Roughness
  9. 12. Surface Roughness <ul><li>Assume Surface is a collection of planar facets </li></ul><ul><li>=> At high magnification, each pixel images one facet </li></ul><ul><li>=> At low magnification, each pixel images many facets </li></ul>
  10. 13. Surface Roughness
  11. 14. Surface Roughness <ul><li>Modelled as a series of V-cavities whose upper edges are in the same plane. </li></ul><ul><li>The width of each facet is assumed to be small compared to its length. </li></ul><ul><li>Torrance-Sparrow Model (1967) </li></ul>
  12. 15. Geometric Attenuation Factor ( GAF ) <ul><li>If surface not illuminated from normal direction, suffers from shadowing and masking </li></ul><ul><li>SHADOWING : Facet partially illuminated, adjacent facet casts shadow </li></ul><ul><li>MASKING : Facet partially visible, adjacent facet occludes. </li></ul>
  13. 16. Geometric Attenuation Factor ( GAF )
  14. 17. Interreflection Factor <ul><li>Experiments suggest too important to be ignored in model </li></ul><ul><li>But, energy in incident light ray diminishes after each bounce </li></ul><ul><li>So, only two-bounce interreflections modelled </li></ul>
  15. 18. Interreflection Factor
  16. 19. Surface Types Modelled <ul><li>Uni-directional Single-Slope Distribution </li></ul><ul><li>Isotropic Single-Slope Distribution </li></ul><ul><li>Gaussian Distribution </li></ul>
  17. 20. Gaussian Distribution
  18. 21. Qualitative Model <ul><li>Model is being used to calculate surface orientation, reflectance and roughness, etc. </li></ul><ul><li>To be tractable, model must use maths that can be easily inverted </li></ul><ul><li>This is why Lambertian is so popular after 240 years </li></ul><ul><li>Must sacrifice accuracy </li></ul>
  19. 22. Qualitative Model
  20. 23. Experiments
  21. 24. Wall Plaster
  22. 25. Sand Paper
  23. 26. Wood Shaving
  24. 27. Conclusions <ul><li>Rough surfaces are inherently non-Lambertian in reflectance </li></ul><ul><li>New model could be used in graphics for realistic rendering </li></ul><ul><li>Model can be used to measure surface roughness </li></ul><ul><li>If rough object can appear flat </li></ul>
  25. 28. Future Work <ul><li>Simultaneous recovery of shape and roughness </li></ul>
  26. 29. Specular Reflectance
  27. 30. Two Main Problems <ul><li>Detecting specularities </li></ul><ul><li>Real Features </li></ul><ul><li>Virtual Features </li></ul><ul><li>Shape recovery of specular surfaces </li></ul><ul><li>Many pitfalls </li></ul>
  28. 31. Result not on Surface !!!!!
  29. 32. Real & Virtual Features
  30. 33. Approach <ul><li>Use virtual features to help recover shape </li></ul><ul><li>Model in 2D domain first </li></ul><ul><li>And generalize to 3D domain </li></ul>
  31. 34. Recovery of 2D Surface Profile <ul><li>Curve Representation </li></ul><ul><li>Important to simplify analysis </li></ul><ul><li>Cartesian Coordinates - represents curve as series of points (but no local slope) </li></ul><ul><li>Legendre Transform – represents curve as an envelope of tangents </li></ul>
  32. 35. Curve Representation
  33. 36. Caustics <ul><li>Virtual feature travels on a surface producing a family of reflection rays </li></ul><ul><li>The envelope defined by the family is called the caustic </li></ul><ul><li>Use caustic compactness to distinguish real from virtual feature </li></ul>
  34. 37. Caustics
  35. 38. 2D Surface Recovery <ul><li>Develop equations to recover profile to one parameter family of equations </li></ul><ul><li>May determine correct profile by tracks two virtual features </li></ul>
  36. 39. Results
  37. 40. Results
  38. 41. 3D Motion <ul><li>Generalizes 2D concepts of caustics & equations </li></ul>
  39. 42. 3D Motion
  40. 43. 3D Caustics
  41. 44. Results
  42. 45. Conclusions & Future Work <ul><li>Closed-form relationship between image trajectory of a virtual feature and surface profile </li></ul><ul><li>New technique for detecting virtual features </li></ul><ul><li>Future Work </li></ul><ul><li>Fusion of real & virtual feaure </li></ul><ul><li>Shape recovery of rough surfaces </li></ul>