Bernal S D_2010.pdf - University of Plymouth

4.2. ARCmm^TVRES
S3
I
C2
s
f
S2
o o
O I .
P(C2 I S3)
4 nodes
I
(= 60 objects)
9 nodes
Kc} states
P(S2b„..«,».|C2)
OS lo o|p jo o!0"lo OS "to oo to ojfl to o;6 lo o
o-
Ol Ol
O:
o o
E „M
o o
E„ = 3
0 O O O o o
E i, = 8
o b
o o iu-* Ej=5
2,253 nodes
(=1000
features)
Figure 4.5: Bayesian network reprodueing the shaicture and functionality of a modified version
ol' the :Mevel HMAX model (Serre c1 al. 2()()7c), The variation consisis of a
reduced pooling range (RF size) at the lop layers C2 and S3 and was introduced
to try to improve the recognition of the translated set of inpui objects. Only ihosc
layers a hove S 2 are shown, as layers SI and CM are equivalent to the previous version.
The numher of nodes at each layer and hand depends on the size of the input
image and Ihe pooling (AN) and sampling (r) parameters. The figure shows ihe
total numher of nodes at eaeli layer, assuming an input image ol 160x160 pixels
and the parameters defined hy Table 4.:^. Fach nixle in Ihe network has an a-ssiKialed
CPT which links it wiih ii.s parent nodes. Similarly, each node perform.s the
same internal operations, shown in Figure 4.^. which correspond to the distributed
implementation of belief propagation.
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