understanding the mesopic vision
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Understanding the mesopic vision. Zoltán Vas Department of Image Processing and Neurocomputing University of Pannonia Hungary. Aims. Give a model to describe the mesopic luminance range Achieve a safe detection threshold prediction - PowerPoint PPT PresentationTRANSCRIPT
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Understanding the mesopic vision
Zoltán Vas
Department of Image Processing and NeurocomputingUniversity of Pannonia
Hungary
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Aims
Give a model to describe the mesopic luminance range
Achieve a safe detection threshold prediction
Based on this model, optimizing traffic lighting, car headlights
Give better experimental methods to model the mesopic range
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Experimental method I.
Large achromatic background, illuminated by white phosphor LEDs (CCT=6000K, x=0.32,y=0.34), L=0.5cd/m2
Visual targets generated by two projectors of the same kind (HP V6210 DLP)
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Experimental method II.
Two mayor methods: Fixed-step staircase (with increments, one up /one down rule) Quasi-stationer
Primary visual target: 2° filled disk at 20° eccentricity
Secondary (control) visual target: 2° red number, on-axis
Quasi-monochromatic: 440nm, 490nm, 540nm , 570nm, 600nm, 615nm (Half Band Width: 10nm)
Additive mixture of 615nm and 540nm, 615nm and 440nm, 490nm and 600 (two-peaks)
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Trials I. Series 1-2.
In the first and second series the 490nm, 540nm, 615nm central wavelength HBW color filters were used, for quasi-monochromatic target
The additive mixture of these (490nm+615nm, and 540nm+615nm) was the two-peak target
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Trials II. Series 3.
In these series the 490nm and 600nm central wavelength HBW color filters were used, to test the achromatic response
The T=aL-bM, D=cL+dM-eS, A=fL+gM, were used
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Trials III. Series 4.
In the 4th series the same filters were used, as in the 3rd
The FSS staircase method was used, without control target (possible adaptation conflict)
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Trials IV. Series 5.
The data of the additive mixture of 540nm+615nm central wavelength HBW color filters were compared with a quasi-monochromatic color filter
This quasi-monochromatic filter was assessed by an Excel script(570nm)
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Trials – in the future
The 6th series start in October, a new method will be used, which is not the FSS staircase, with color filters used in Darmstadt too
A new experiment will start, modeling the dynamic background, psychological influence by using a realistic driving simulator
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Models until today
By using the V(λ) and V’(λ) as the base, the model will have uncertainty (because of the additivity error, caused by the spectral integration)
Mayor models: Move model „X” model Intermediate model
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CHC model
Based on the: L,M,S cone fundamentals V’(λ) function V*(λ) function (the Sharp et al photopic v.f.) Cone opponent channels included
For visual targets : quasi-stationer (over 2 s) 2°(or similar) visual targets on a large, uniform
background
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CHC model and results
The photopic-type models predict higher, than the real threshold. This is caused by the spectral integration
CHC predicts better => safer detection Another advantage is, that we can plot the
Vmes,CHC(λ), which is the spectral sensitivity curve for observer-to-observer (it has more local maxima)
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BUT!
The FSS staircase method can only be used with care for mesopic range detection tasks
The observers are influenced by nearly everything, e.g. temperature, mood, time…And that’s why we need a quicker but also precise method to understand better this visual range
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Comparison of Staircase and Multi-step case method (MSC)
Miguel A. García-Pérez dealt with the FSS staircase method (Forced-choice staircase with fixed step sizes: asymptotic and small-sample properties, Vision Research 38. 1998)
It’s a good method, but for the mesopic scenario it can be used with care
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Staircase I. - basics
D: set of events that trigger a step down U: set of events that trigger a step up : is a monotonic increasing psychometric
function Prob(D|x), Prob(U|x) are the probabilities of a step
down and up, at a stimulus level x, and there is a value x0 such that Prob(D| x0)=Prob(U| x0)
: is the step size
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Up/down step variations
Dixon and Mood’s u/d method At every correct answer down, every wrong answer up.
Wetherill and Levitt’s transformed u/d method There are several sequences of responses over various
numbers of consecutive trials, but the up/down continue to be identical size
Karenbach’s weighted up/down method This is like the Dixon and Mood’s method, but the step size
down differs from the step size up Transformed and weighted up/down method
Combining the non-unitary sets D,U (like transformed), with equal sizes for the steps up, down (like weighted).
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Psychometric functions I.
Where pl is the lapsing level, pg is the guessing level, F(x) is the probability of a psychometrical outcome at stimuli level x (in the following the x is replaced with m(Michelson-contrast), so the photometric function is restricted to 0≤m ≤ 1)
This function expresses the probability of a correct response, as a sum of the probabilities of detecting the patter and not lapsing (first summand), and not detecting, but guessing correctly (second summand).
( ) (1 ) ( ) 1 ( )l gx p F x p F x
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Psychometric functions II.
For F, every function can be used, which qualifies as a cumulative distribution function.
García-Pérez used for F the Weibull function, so:
where α is the spread, and β is the location
( ) 1 (1 )exp , 0,1l l g
mm p p p m
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Psychometric functions III.
From the presumed
convergence probability can be calculated
A convergence contrast was computed as the arithmetic mean of the distribution, and its standard deviation was used, to compute a convergence contrast interval with boundaries defined ± standard deviation away from the convergence contrast.
10( )x f
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Psychometric functions IV.
Convergence percent-correct was determined by entering the convergence contrast into the psychometric function used in that run, and expressing the probability associated with it as a percentage, and a convergence percent-contrast interval was analogously obtained from the boundaries of the convergence contrast interval.
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FSS staircase rules I.
They’ve tried out more step sizes, and methods: one-, two-, three-, four down/ one up and they got following rules: If > , the asymptotic convergence
approaches the guessing level if the step size increases
If = , the asymptotic convergence dependes on the starting value, but it begins a big fluctuation if the relative step size increases
If < the asymptotic convergence is largly invarinat, if the step size increases
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Multi-step case method (MSC)
Based on experience in mesopic-trials The FSS staircase method converged not
fast enough That’s why I had to develop a new method, to
increase the performance of the convergence, and to decrease the time needed
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MSC basic rules & step sizes
Preliminary phase (to assess the staring value) Multiple step sizes Adaptive choice between the step sizes based
on the performance of the observer Groups presented Steps:
Percentage % 100 75 50 25 0
Step size -3 -1 Repeat 1 3
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MSC – percent-correct point
Near the threshold the number of 50% responses get more often
After 50% response was given twice after the other, the last ten groups will be shown
From this 40 responses the percent-correct point can be calculated by using the given responses
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MSC vs. FSS staircase method I.
FSS staircase + Is more described by mathematical equations, Can calculate p.-c. p. Lot of people use this method
FSS staircase – Difficult to use Complicated equations Takes sometimes a long time to converge
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MSC vs. FSS staircase method II.
MSC + Quick converge Simple equations Simple method to calculate p.-c. p. Using preliminary phase the convergence begins
from near the threshold value Based on experience
MSC – Not described by mathematic equations yet Not tested in other tasks
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Thank you for your attention!
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Plans for the future
Reproduction of the experiments in TUD
Comparing data, observers, understanding spatial influence
Experiments in „real-life” scenarios