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Computer Vision Aids for the Blind and Low-Vision Patients

Computer Vision Aids for the Blind and Low-Vision Patients

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Computer Vision Aids for the Blind and Low-Vision Patients

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  1. Computer Vision Aids for the Blind and Low-Vision Patients Itai Segall & Ron Merom Advanced Topics in Computer Vision Seminar April 3rd, 2005

  2. Introduction • 180 Million people worldwide, who are visually disabled. 45 Million legally blind. [Vision 2020, 2000] • This number is expected to double by the year 2020. [Vision 2020, 2000] • Efforts are made in various fields to help people with visual impairments.

  3. Types of Visual Impairments • Scotomas

  4. Types of Visual Impairments • Scotomas • CFL (Central Field Loss)

  5. Types of Visual Impairments • Scotomas • CFL (Central Field Loss) • PFL (Peripheral Field Loss)

  6. Types of Visual Impairments • Scotomas • CFL (Central Field Loss) • PFL (Peripheral Field Loss) • Hemianopia

  7. Types of Visual Impairments • Scotomas • CFL (Central Field Loss) • PFL (Peripheral Field Loss) • Hemianopia • Total Blindness

  8. Lecture Outline • Studying the problem • Suggested Solutions • Eyewear • Enhancement of TV images • Navigation Aids

  9. Studying the Problem Example: How Does the Visual System Deal with Scotomas ? [D. Zur, S. Ullman, 2002]

  10. What is a Scotoma? • Retinal scotomas can be caused by various diseases such as age-related macular degeneration (AMD) • “Visual scientists sometimes pass their time during a boring lecture by staring at a light on the ceiling until it produces a vivid afterimage. The afterimage can be used to blot out the lecturer’s head.”1 1 Morgan, M. “Making holes in the visual world”, 1999

  11. Filling-in of Visual Patterns • Patients with small enough scotomas perceive the world as uninterrupted • Question: how does the visual system deal with missing information? • eye movements • ignored • filled in

  12. Filling-in of Visual Patterns – cont. • Why study it? • Better understanding of the visual system • Study can lead to developing visual aids • Blind Spot • Extensively studied

  13. Experiment • Subjects: patients with scotomas • Show various visual patterns • Short period of time (400ms) • Patients were asked to: • 1. Rate uniformity • 2. When designated as non-uniform, choose: • Blur • Straightness • Contrast

  14. Results Pattern Report

  15. Results

  16. Results Vs.

  17. Conclusions • Missing information is filled-in, not ignored • Higher density  Better filling-in • Higher regularity of stimulus  Better filling-in

  18. Lecture Outline • Studying the problem • Suggested Solutions • Eyewear • Enhancement of TV images • Navigation Aids

  19. Eyewear – classical solutions • CFL-Magnifying Devices • PFL-Minifying Devices • Hemianopia – Binocular sector prisms

  20. Eyewear • Problem: these solutions correct one problem while creating another one • Multiplexing approach: [Peli, 2001] • Combine a few information streams • But make sure they can be separated by the visual system • Types of multiplexing: • Temporal • Spatial • Bi-ocular • Composite

  21. Temporal Multiplexing • Different signals at different times • Healthy people use temporal multiplexing Bioptic Telescope (for CFL)

  22. Spatial Multiplexing • Show different information in different parts of the field of view Micro-Telescope (for CFL)

  23. Bi-Ocular Multiplexing • Expose each eye to different information • May seem too confusing, but experiments show patients adapt Implantable Miniaturized Telescope (for CFL)

  24. Composite Multiplexing Devices that implement more than one type of multiplexing Peripheral Monocular Prism (for Hemianopia)

  25. Composite Multiplexing - cont

  26. Composite Multiplexing - cont • Peripheral Monocular Prism combine: • Bi-ocular multiplexing • Spatial multiplexing • Spectral multiplexing

  27. Composite Multiplexing 2 Minified Contours Augmented View A computer-aided device for PFL

  28. Composite Multiplexing 2 - cont

  29. Lecture Outline • Studying the problem • Suggested Solutions • Eyewear • Enhancement of TV images • Navigation Aids

  30. Enhancement of TV Images • TV serves as an important medium for retrieving information, entertainment and education • Visual impairments make watching TV difficult

  31. Enhancement of TV Images – cont. • Previous experiments: enhance high frequencies • But, studies show that the periphery is more sensitive to wideband enhancements • CFL patients need a different solution • Idea: explicitly emphasize edges and bars in the image domain [Peli et al, 2004]

  32. Enhancement of TV Images - cont. First – detect edge & bars [Peli, 2002]: • Use a visual system-based algorithm • Morrone, Burr ’88:edges and bars are where Fourier components come into phase with each other.  In order to find edges and bars, look for phase congruency =

  33. Enhancement of TV Images – cont. • Simplified feature detection algorithm: • Find congruent polarities instead of congruent phases of Fourier components

  34. Algorithm for edge & bar detection Apply bandpass filters Binarize results = + +

  35. Algorithm for edge & bar detection Apply bandpass filters Binarize results Find congruencies = + +

  36. Algorithm for edge & bar detection Apply bandpass filters Binarize results Find congruencies =

  37. Algorithm for edge & bar detection Apply bandpass filters Binarize results Find congruencies =

  38. Enhancement of TV Images – cont. A more interesting example:

  39. Wideband enhancement algorithm • Create feature map • Substitute/Add map to original image • Features can be weighted according to their magnitude

  40. Low EnhancementLevel

  41. Medium Enhancement Level

  42. High Enhancement Level

  43. Medium Enhancement Level

  44. High Enhancement Level

  45. Enhancement of TV Images – Experimental Results • Most CFL patients selected a slightly enhanced image • But… when asked to compare it to the original image, they didn’t find it to be much better  • Why? • Any enhancement necessarily distorts the image • High contrast features were enhanced much more than moderate ones

  46. Lecture Outline • Studying the problem • Suggested Solutions • Eyewear • Enhancement of TV images • Navigation Aids

  47. Navigation Aids • Classics: a cane & a guide dog • Will discuss two solutions • Specific – locate & recognize signs • General – first steps towards an “inter-sensory” solution

  48. Sign finding • “Talking Signs” • Obvious problem: should be installed • Suggested solution: Signfinder [Yuille et al., 1999] • as an example, we’ll discuss (American) stop signs

  49. What does it take to be a stop sign? • Being red and white? • Being octagonal?

  50. Then how to find stop signs? • Assumptions: • Two-colored • Stereotypically shaped • There exists a set of typical illuminants • Preprocessing – find this set