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CS 395/495-25: Spring 2004

CS 395/495-25: Spring 2004. IBMR: Poisson Solvers Can Reconstruct Images from their Changes Jack Tumblin jet@cs.northwestern.edu. Do pixels describe what we see?. What We Want. What We Get. What do you see?. A. B. What part has constant intensity?. What do you see?.

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CS 395/495-25: Spring 2004

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  1. CS 395/495-25: Spring 2004 IBMR:Poisson SolversCan Reconstruct Images from their Changes Jack Tumblin jet@cs.northwestern.edu

  2. Do pixels describe what we see? What We Want What We Get

  3. What do you see? A B What part has constant intensity?

  4. What do you see? Humans don’t sense intensities reliably, but infer them from changes A B B intensity is constant, A is darker on right

  5. What do you see? Humans don’t sense intensities reliably, but infer them from changes A B (tol’djah!)

  6. What do you see? X Y What part has constant intensity?

  7. What do you see? Humans don’t sense intensities reliably, but infer them from changes X Y What part has constant intensity? NEITHER!

  8. What do you see? Humans don’t sense intensities reliably, but infer them from changes X Y Constant What part has constant intensity? NEITHER!

  9. What do you see? Humans don’t sense intensities reliably, but infer them from changes Example: aren’t all the dots white? (http://udel.edu/~jgephart/fun2.htm)

  10. Why Pixels Could be Improved: • People see (or think they see) changesfinite features that may have infinite bandwidth occlusion, depth, collision time, trajectory changes, corner, cone tip, boundaries, edges, occlusions, shadow details, contact points, velocity & direction changes... Pixels only approximate changes, and approximate discontinuous changes poorly; object boundaries, silhouettes, etc. They force indirect estimation...

  11. How? Retinal Receptive Fields… - - - - - + - - - + + + + + + + + - • 130M Photoreceptors1M optic nerve fibers • Center-Surround Antagonism:Out  Center - (avg surround) • Complementary ON-center, OFF-center types • Center responds quickly; Surround responds more slowly • Output: ‘recent local change’

  12. Complementary Receptive Fields - - - - - + - - - Firing Rate (Hz) + 100 + + + + + 50 + + 10 ctr/surr - 10 0.1 1 ctr/surr 10 50 Firing Rate (Hz) 100 • Retina is ~differential for small signals • Better SNR • Can signal ambiguity (eyes closed, etc) • Allows quality/fault detection

  13. Yarbus (1950s): Pioneer of Retinal Stabilization Experiments (inspired a flood of others…)

  14. Strongly Implies ‘Filling In’ requires Nystagmus for temporal transients... ‘mm, nothing much. (green-ish?)’ ‘Not much to see.(pink-ish?)’ ‘BUT HERE is a Big ring of VERY strong change!’

  15. ‘mm, Not much to see. (green-ish?)’ ‘Not much to see.(pink-ish?)’

  16. What ‘Changes’ do we Sense? • Intensity (luminance) vs. local position • Color (chrominance) vs. local position • Intensity vs. time (‘flicker’) • Color vs. time • VERY weak, low-res: overall intensity • Inertial changes: movement, velocity… Compensated eye moves (saccade, glissade, smooth-pursuit… • Higher-level attributes? Umm, er, uh,….

  17. ‘Digital’ Image: a 2D Grid of Numbers • NO intrinsic meaning—use it for anything: reflectance, transparency, illumination, normal direction, material, velocity. BUT usually ‘intensity’ y y x x

  18. 2D Images Described by Change? • Image intensity as height field f(x,y): • 1st derivative— Gradient: the ‘uphill’ vector at point x,y = f(x,y) = (f(x,y)/x, f(x,y)/y) = f y x f(x,y)

  19. 2D Images Described by Change? • Image intensity as height field f(x,y): • 2st derivative— Gradient: the ‘uphill’ vector at point x,y = f(x,y) = (f(x,y)/x, f(x,y)/y) = f y x f(x,y)

  20. Review: Div, Grad and Curl • Formalized, computable ‘Local Change’

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