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Low-Level Vision

Low-Level Vision. Low Level Vision--outline. Problem to be solved Example of one computation—lines How simple computations yield more complex information. Problems to be solved. Problem 1: Indeterminacies Problem 2: The input to resolve these indeterminacies is impoverished.

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Low-Level Vision

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  1. Low-Level Vision

  2. Low Level Vision--outline • Problem to be solved • Example of one computation—lines • How simple computations yield more complex information

  3. Problems to be solved Problem 1: Indeterminacies Problem 2: The input to resolve these indeterminacies is impoverished

  4. Indeterminacies Many of the qualities of objects that we would like to know about trade off with other qualities. shape/orientationreflectance/light source/shadow size/distance

  5. Shape/Orientation

  6. Reflectance/Light Source/Shadow This joke turns on the assumption that you will see a shadow, not a difference in reflectance of the object (moon) across its face.

  7. Size/Distance

  8. Problems So problem 1 is that the types of information that we want trade off with one another Problem 2 is that the initial information the visual system has is extremely impoverished

  9. This is the input You end up with the #of objects, their sizes,shapes, distances,textures, motions.

  10. How do you get from one to the other? Researchers divide this question into two parts: Low-level vision: we assume that we can’t get much information out of this array of intensity values.There must be algorithms that summarize this info.High-level vision: taking the output of the low-level processes and transforming it to get objects & their properties.

  11. Simple computation 35 35 35 5 5 5 35 35 35 5 5 5 35 35 35 5 5 5 35 35 35 5 5 5 35 35 35 5 5 5 35 35 35 5 5 5 You saw this before. . . . Can you tell what this is?

  12. Crucial summary--find edges An edge is a sudden discontinuity in intensity. 35 35 35 5 5 5 35 35 35 5 5 5 35 35 35 5 5 5 35 35 35 5 5 5 35 35 35 5 5 5 35 35 35 5 5 5

  13. Why edges? Edges frequently correspond to the boundaries of objects; a map of edges is a good start to identifying objects. Edges are invariant to lighting conditions.

  14. How to find edges? Computationallyeasy to find discontinuities Compare means of adjacent columns, rows, diagonals

  15. What about textures? Why don’t you see a million objects when you see a hat with many “edges” (Herringbone pattern)?

  16. Assess at more than one scale Assess neighboring columns: yields five edges

  17. Assess at more than one scale Assess every three columns (i.e., take the mean) yields one edge

  18. Biological evidence

  19. Retina

  20. Ganglion cells: center-surround

  21. On-off can combine to form line detectors

  22. Or an edge detector

  23. Hubel & Wiesel’s experiments

  24. Biological evidence It does seem that some of the cells relatively early in the visual processing stream care about edges.

  25. All of this was about lines. Now how do you get distance, shapes, etc.

  26. Shape/orientation indeterminacy Perkin’s laws--conjunctions of lines assumed to correspond to different 3D shapes.

  27. Perkins’ Laws Possibly built into early visual processing. Pop-out with perkins’ laws type angles, but not with other angles.

  28. That’s it for lines Focus on other assumptions

  29. Light source/reflectance/shadow What’s this?

  30. Assumption 1: surfaces are uniformly colored. (That’swhy shading gives the impression of 3 dimensions. Shading is assumed to be due to hills & valleys.

  31. Light/reflectance/shadow Shadow: light is assumed to be coming from above.

  32. Reflectance/Light Source/Shadow

  33. How is constancy figured out? Obviously, absolute constancy is not calculated

  34. Local contrast Assumption 3: the brightest thing around is white; the darkest thing around is black.

  35. Distance/size Isn’t it the case that we frequently just know the size an object should be? This is familiar size and it’s actually not that powerful a cue.

  36. Familiar size When you remove the cue of height in the picture planethe person looks tiny.

  37. Cues to distance Convergence--not very effective More effective are a range of cues that can be evaluatedin a picture plane, and so are often called pictorial cues.

  38. Occlusion

  39. Texture Gradient

  40. Linear perspective

  41. Height in picture plane

  42. Atmospheric Perspective

  43. Stereopsis Very important cue. This is NOT a pictorial cue. Based on the fact that the two eyes get slightly different views of the world.

  44. Stereopsis Difference between what the left eye and right eye sees is called retinal disparity. Farther object, less difference

  45. Stereopsis Problem: how do you match up the views of the two retinas if objects are similar? This is called the correspondence problem. (Consider that highlights differ because of different reflectionsand there are geometric distortions due to seeing things froma different angle.)

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