Computational Perception. Visual Coding 3

Transcription

1 Computational Perception /785 February 21, 2008 Visual Coding 3

2 A gap in the theory? from Hubel,

3 Eye anatomy from Hubel, 1995 Photoreceptors: rods (night vision) and cones (day vision) Other layers: processing to enhance SNR and maximize information transmission All visual information conveyed to brain through ~1 million optic nerve fibers CP08:Visual Coding 3 3

4 Retinal cross section: the real thing from Wandell,

5 Distribution of rods and cones from Rodeick, 1998 Maximum accuity is in the fovea: - about 50,000 cones, each about 30 seconds of visual angle - no sharp border, but about 250 cones across - about 2 of visual angle CP08:Visual Coding 3 5

6 Photoreceptor mosaic in macaque monkey Light circles S cones. Dark circles M&L cones. Small circles rods. from Wandell, 1995 CP08:Visual Coding 3 6

7 Light intensity spans an enormous range luminance catch rate (cd/m 2 ) (photons/s) environment significance /5100 s overcast night sky rod threshold /5 s starlight 3/s cone threshold 10 2 full moon 10/s colors visible /s sunrise 10 2 indoor lighting 10 3 best accuity 10 4 clear sky at noon ,000/s patch of snow at noon 1,000,000/s maximal cone rate 7

8 Scotopic Vision: rods and night vision from Rodeick, 1998 Photons on retina: sky-tree border in overcast night sky 8

9 Rods sensitivity Rods respond reliably to single photons. - Do we really need this degree of sensitivity? Suppose instead of 1 photon, a rod needed 2 photons (within 0.1 sec). - How much more light would we need to get same output? Relevant facts: - photon catch rate is 1/5100 secs or once every 85 hours - but, there are 10 8 rods - photons arrive at random times Poisson distribution What is the rate of seeing two photon within 0.1 sec? - Once every 16 years. Need 100,000 times intensity to achieve same discriminability. - In short: two photon sensitivity no night vision. 9

10 Dynamic range of rods from Rodeick, 1998 Retinal circuitry enhances sensitivity by summing the output across many rods Rods are also insensitive to the direction of light What about cones? CP08:Visual Coding 3 10

11 Dynamic range of rods and cones from Rodeick, 1998 Cones do not saturate CP08:Visual Coding 3 11

12 Cones are adapted for daylight vision Cones are sensitive to the direction of photon arrival from Rodeick, 1998 Cones are aligned to optimize focus 12

13 Color vision: Cone spectral sensitivity curves from Wandell, 1995 Three types of spectral sensitivity curves: long, medium, and short wavelength. Response of an individual cone is the integral of the product of the curve and the spectrum at that point. CP08:Visual Coding 3 13

14 Sampling of color space from Wandell, 1995 These two spectra are indistinguishable. The perception of color starts from here, but gets far more complicated. CP08:Visual Coding 3 14

15 There are far more M&L cones than S cones: Why? Light circles S cones. Dark circles M&L cones. Small circles rods. from Wandell, 1995 CP08:Visual Coding 3 15

16 Chromatic aberration from Wandell,

17 Focus is matched to M & L cones from Rodeick,

18 Modulation transfer function 3 mm pupil diameter. Eye in focus at 580 nm and diffraction limited. No significant contrast beyond 4 cpd at short wavelengths. CP08:Visual Coding 3 18

19 Cone spatial mosaic measured in living human retina Roorda and Williams (1999). False color: blue, green, red represent S, M, and L cones. Subject JW temporal (a) and nasal (b) retina, Subject AN nasal retina (c) Subject % L cones % M cones % S cones L:M ratio JW : 1 AN : 1 CP08:Visual Coding 3 19

20 Back to computation

21 Redundancy reduction for noisy channels (Atick, 1992) s input channel y = x + ν x recoding A Ax output channel y = Ax + δ y Mutual information I(x, s) = s,x [ ] P (x, s) P (x, s) log 2 P (s)p (x) I(x, s) = 0 iff P (x, s) = P (x)p (s), i.e. x and s are independent. 21

22 Profiles of optimal filters high SNR - reduce redundancy - center-surround low SNR - average - low-pass filter matches behavior of retinal ganglion cells CP08:Visual Coding 3 22

23 An observation: Contrast sensitivity of ganglion cells Luminance level decreases one log unit each time we go to lower curve. 23

24 Natural images have a 1/f amplitude spectrum Field, 1987 CP08:Visual Coding 3 24

25 Components of predicted filters whitening filter low-pass filter optimal filter from Atick,

26 Predicted contrast sensitivity functions match neural data from Atick,

27 Robust coding of natural images Theory refined: - image is noisy and blurred - neural population size changes - neurons are noisy from Hubel, 1995 (a) Undistorted image (b) Fovea retinal image (c) 40 degrees eccentricity Intensity Visual angle [arc min] from Doi and Lewicki, 2006 CP08:Visual Coding 3 27

28 Problem 1: Real neurons are noisy Estimates of neural information capacity system (area) stimulus bits / sec bits / spike fly visual (H1) motion 64 ~1 monkey visual (MT) motion frog auditory (auditory nerve) noise & call 46 & & 7.8 Salamander visual (ganglinon cells) rand. spots cricket cercal (sensory afferent) mech. motion cricket cercal (sensory afferent) wind noise cricket cercal (10-2 and 10-3) wind noise 8-80 avg. = 1 Electric fish (P-afferent) amp. modulation Limited capacity neural codes need to be robust. After Borst and Theunissen,

29 Traditional codes are not robust encoding neurons Original sensory input 29

30 Traditional codes are not robust encoding neurons sensory input Add noise equivalent to 1 bit precision Original 1x efficient coding reconstruction (34% error) 1 bit precision 30

31 Redundancy plays an important role in neural coding Response of salamander retinal ganglion cells to natural movies fractional redundancy cell pair distance (μm) 31 From Puchalla et al, 2005

32 Robust coding (Doi and Lewicki, 2005,2006) x W u A x^ Data Encoder Noiseless Representation Decoder Reconstruction Model limited precision using additive channel noise: x W u r A x^ Data Encoder Noiseless Representation Channel Noise n Noisy Representation Decoder Reconstruction r = Wx + n = u + n. ˆx = Ar = AWx + An. Caveat: linear, but can evaluate for different noise levels. 32

33 Generalizing the model: sensory noise and optical blur (a) Undistorted image (b) Fovea retinal image (c) 40 degrees eccentricity Intensity Visual angle [arc min] s optical blur H sensory noise channel noise ν δ encoder x W r only implicit decoder A ŝ image observation representation reconstruction Can also add sparseness and resource constraints 33

34 How do we learn robust codes? sensory noise channel noise ν δ optical blur encoder decoder s H x W r A ŝ image observation representation reconstruction Objective: Find W and A that minimize reconstruction error. Channel capacity of the i th neuron: C i = 1 2 ln(snr i + 1) To limit capacity, fix the coefficient signal to noise ratio: SNR i = u2 i σ 2 n Now robust coding is formulated as a constrained optimization problem. 34

35 Robust coding of natural images encoding neurons sensory input Add noise equivalent to 1 bit precision Original 1x efficient coding reconstruction (34% error) 1 bit precision 35

36 Robust coding of natural images encoding neurons sensory input Weights adapted for optimal robustness Original 1x robust coding reconstruction (3.8% error) 1 bit precision 36

37 Reconstruction improves by adding neurons encoding neurons sensory input Weights adapted for optimal robustness Original reconstruction error: 0.6% 8x robust coding 1 bit precision 37

38 Adding precision to 1x robust coding original images 1 bit: 12.5% error 2 bit: 3.1% error 38

39 Can derive minimum theoretical average error bound E = 1 1 N SNR + 1 N M λ i tha - ith eigenvalue of the data covariance N - input dimensionality [ N ] 2 λi i=1 M - # of coding units (neurons) if SNR SNR c Algorithm achieves theoretical lower bound Results Bound 0.5x 19.9% 20.3% 1x 12.4% 12.5% 8x 2.0% 2.0% Balcan, Doi, and Lewicki, 2007; Balcan and Lewicki,

40 Sparseness localizes the vectors and increases coding efficiency encoding vectors (W) decoding vectors (A) normalized histogram of coeff. kurtosis Kurtosis of RC avg. = 4.9 robust coding % frequency % robust sparse coding average error is unchanged 12.5% 12.5% % kurtosis kurtosis Kurtosis of RSC kurtosis avg. = 7.3

41 Non-zero resource constraints localize weights fovea (a) 20dB 10dB 0dB -10dB (b) (c) 40 degrees 20dB average error is also unchanged -10dB 41 from Doi and Lewicki, 2007