[Gzz-commits] gzz/Documentation/Manuscripts/Paper paper.tex

[Janne V. Kujala]

CVSROOT:        /cvsroot/gzz
Module name:    gzz
Changes by:     Janne V. Kujala <address@hidden>        02/11/20 10:14:13

Modified files:
        Documentation/Manuscripts/Paper: paper.tex 

Log message:
        more of the model

CVSWeb URLs:
http://savannah.gnu.org/cgi-bin/viewcvs/gzz/gzz/Documentation/Manuscripts/Paper/paper.tex.diff?tr1=1.48&tr2=1.49&r1=text&r2=text

Patches:
Index: gzz/Documentation/Manuscripts/Paper/paper.tex
diff -u gzz/Documentation/Manuscripts/Paper/paper.tex:1.48 
gzz/Documentation/Manuscripts/Paper/paper.tex:1.49
--- gzz/Documentation/Manuscripts/Paper/paper.tex:1.48  Wed Nov 20 08:18:14 2002
+++ gzz/Documentation/Manuscripts/Paper/paper.tex       Wed Nov 20 10:14:13 2002
@@ -226,17 +226,24 @@
 For example, contours are formed from consistent directions 
 of adjacent receptive fields.
 
+In \cite{schweitzer83texturing}, simple local features in artificial
+textures are used for implying global surface shape. XXX
+
 The higher levels of visual processing are no longer hierarchical
 and not throughly understood.
 We simply assume that the intensities of different features,
 such as local and global shapes and colors, form a \emph{feature vector},
 which facilitates recognition and memorization of images.
 
-distinguishability: should produce random vector in brain 
-    (perception model in Fig.~\ref{fig-perceptual}) -- saving of bits
+For the textures to be distinguishable, they should produce
+distinct, random feature vectors in brain. 
+In a sense, the perception model should invert the
+visual processing to produce a unique texture from 
+a random vector seeded by the identity (see Fig.~\ref{fig-perceptual}).
+%We call this the principle of saving bits.
 
-In \cite{schweitzer83texturing}, simple local features in artificial
-textures are used for implying global surface shape.
+%distinguishability: should produce random vector in brain 
+%    (perception model in Fig.~\ref{fig-perceptual}) -- saving of bits
 
 \begin{figure}
 \centering
@@ -250,12 +257,11 @@
 
 The model explains easily why uniformly random texels (white noise)
 would not make easily distinguishable patterns: different instances
-of noise would all yield almost exactly the same 
-% pattern at the XXX
-feature vector in brain:
+of noise would all yield almost exactly the same feature vector in brain:
 Noise has no global shape because there is no correlation between
 the random local features; it is simply perceived as the distribution
-of the local features (color and overall frequency (the size of texels)).
+of the local features, i.e., color and overall frequency 
+(the density of texels).
 
 Features independent at XXX should not correlate between XXX; for example,
 if all circles were green and all squares yellow, a considerable amount of