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Bernal S D_2010.pdf - University of Plymouth

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3.4. EXISTING MODELS<br />

Bollom-up cues<br />

Frorn illuminalea B1<br />

part <strong>of</strong> the face<br />

High-level representalion<br />

with aDsIract fealures<br />

Low-level re prose nia lion<br />

with high resolution<br />

Top-down foe d back<br />

B2 recognizes shadowed<br />

edge as part <strong>of</strong> the fece<br />

I I P(X3/X4)<br />

x,= P(x^x,) • P(x,;x.)<br />

X,= P(X,/X,)- P(X^Xj)<br />

Xi<br />

X,= P(X^X,1- P(X,/X,1<br />

T<br />

P(X2/X3)<br />

P(X,/X2)<br />

Figure 3.J J: Bayesian belief prupagat ion archileclure applied lo the visual system, a) Inilially,<br />

bottom-up cues from the illuminated part <strong>of</strong> the face (Bl) cause a face hypothesis<br />

iLi hci'ome aclivaicil ai ihc higher levels. Then inrormaiiim about ihe likely<br />

features and propiirliuns <strong>of</strong> a faue is amveyed through top-down feedback (B2j<br />

lo Ihe lower-level high resoluiion buffer. Re-examiriaiion <strong>of</strong> ihc data results in a<br />

reinierprelalion ol" ihf Faint edj;c in the shadtiwed area as an iiTiportani part <strong>of</strong> the<br />

face contour b] Hacli area conipuios a .set <strong>of</strong> beliefs, Xj, based on boitoin-up sensory<br />

data (X,-])and top-down priors (/'fX/X+i). which are iniegraied according<br />

lo ihe Bayesian inference equation. Beliefs rue continually updated according to<br />

changes in earlier and higher areas to obtain ibc niosl probable distribution <strong>of</strong><br />

causes al each level. Adapicd from Lee and Mumford (2003).<br />

practical implementaiion <strong>of</strong> this theoretical approach was provided by the authors.<br />

Nonetheless, this theoretical paper has strongly inspired and motivated ihc present thesis, and<br />

provides an intuitive example which allows one to heller understand the concept <strong>of</strong> how belief<br />

propagalitin can be applied to visual processing. Consider the shadowed face example shown in<br />

Figure 3.13. Initially, bottom-tip cues from the illuminated part <strong>of</strong> the face cause a/ocf hypoth­<br />

esis to become activated at the higher levels. Then informalion about the likely features and<br />

proportions <strong>of</strong> a face is conveyed through top-down feedback to the lower-level high resolution<br />

buffer. Re-examination <strong>of</strong> the data resuhs in a reinlerpretaiion <strong>of</strong> the faint edge in the shadowed<br />

area as an important part <strong>of</strong> the face contour. This new detailed information can then be used<br />

127

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