[Gzz-commits] manuscripts/Paper paper.tex

[Tuomas J. Lukka]

CVSROOT:        /cvsroot/gzz
Module name:    manuscripts
Changes by:     Tuomas J. Lukka <address@hidden>        03/03/31 05:33:15

Modified files:
        Paper          : paper.tex 

Log message:
        oops

CVSWeb URLs:
http://savannah.gnu.org/cgi-bin/viewcvs/gzz/manuscripts/Paper/paper.tex.diff?tr1=1.80&tr2=1.81&r1=text&r2=text

Patches:
Index: manuscripts/Paper/paper.tex
diff -u manuscripts/Paper/paper.tex:1.80 manuscripts/Paper/paper.tex:1.81
--- manuscripts/Paper/paper.tex:1.80    Mon Mar 31 05:32:22 2003
+++ manuscripts/Paper/paper.tex Mon Mar 31 05:33:15 2003
@@ -299,19 +299,6 @@
 assume certain primitive shapes whose 
 structure facilitates recognition.
 
-The simple model we use here assumes
-that at some point,
-the results from the different pre-attentive feature detectors,
-such as different shapes and colors, 
-are combined to form an abstract \emph{feature vector}
-(see Fig.~\ref{fig-perceptual}).
-The feature vector is then used to compute 
-which concept the particular
-input corresponds to by comparing it to memorized models
-in a simple perceptron-like 
-fashion\cite{rosenblatt62neurodynamics,widrow60adaptive}.
-This configuration is commonly used in neural computation.
-
 
 % In this article, we apply texture shading to synthesize a large number
 % of unique textures for distinguishing virtual objects.
@@ -575,6 +562,19 @@
 %providing an infinite source of unique backgrounds.
 %generating textures based on seed numbers [identity]
 
+The simple model we use here assumes
+that at some point,
+the results from the different pre-attentive feature detectors,
+such as different shapes and colors, 
+are combined to form an abstract \emph{feature vector}
+(see Fig.~\ref{fig-perceptual}).
+The feature vector is then used to compute 
+which concept the particular
+input corresponds to by comparing it to memorized models
+in a simple perceptron-like 
+fashion\cite{rosenblatt62neurodynamics,widrow60adaptive}.
+This configuration is commonly used in neural computation.
+
 \begin{figure}
 %\fbox{\vbox{\vskip 3in}}
 \ifpics
@@ -604,8 +604,7 @@
 % The basic assumption of the model is that an image
 % is perceived as a set of features 
 
-This 
-rough, qualitative 
+This rough, qualitative 
 model is able to explain why uniformly random texels 
 do not make easily distinguishable background textures: 
 after the ``pre-processing'',