[Gzz-commits] manuscripts/Paper paper.tex

[Janne V. Kujala]

CVSROOT:        /cvsroot/gzz
Module name:    manuscripts
Changes by:     Janne V. Kujala <address@hidden>        03/03/20 12:59:02

Modified files:
        Paper          : paper.tex 

Log message:
        pre-attentive notes

CVSWeb URLs:
http://savannah.gnu.org/cgi-bin/viewcvs/gzz/manuscripts/Paper/paper.tex.diff?tr1=1.44&tr2=1.45&r1=text&r2=text

Patches:
Index: manuscripts/Paper/paper.tex
diff -u manuscripts/Paper/paper.tex:1.44 manuscripts/Paper/paper.tex:1.45
--- manuscripts/Paper/paper.tex:1.44    Thu Mar 20 11:59:29 2003
+++ manuscripts/Paper/paper.tex Thu Mar 20 12:59:01 2003
@@ -65,7 +65,7 @@
 to motivate 
 general principles for designing recognizably unique textures
 for use as backgrounds for data.
-To be reconizable,
+To be recongizable,
 the texture should produce a random feature vector in the brain
 {\em after} visual feature extraction. 
 
@@ -301,7 +301,7 @@
 placed (translated) $N$-gon for all different $N$-gons).
 However, the order of similarity in the statistics did not 
 consistently explain discrimination performance, and certain
-distinctive local features were conjectured.
+pre-attentive local features were conjectured.
 
 Julesz\cite{julesz81textons} proposed that texture discrimination could be 
 explained by the densities of textons, fundamental texture elements, such as
@@ -311,12 +311,14 @@
 Much simpler filtering-based models can explain texture discrimination
 just as well \cite{bergen88earlyvision}.
 In these models, a bank of linear filters is applied to the texture followed
-by a nonlinearity and then another set of filters to extract densities
-of features (see, e.g., \cite{heeger95pyramid} for an application).
+by a nonlinearity and then another set of filters to extract features 
+(see, e.g., \cite{heeger95pyramid} for an application).
 %In \cite{heeger95pyramid}, new textures with appearance similar
 %to a given texture are created by matching certain histograms 
 %of filter responses.
 
+XXX: higher-level pre-attetive processes?
+
 %XXX: texture perception reviews
 
 There have been studies on 
@@ -570,7 +572,7 @@
 
 The simple model we use here assumes
 that at some point,
-the results from the  different feature detectors,
+the results from the different pre-attentive feature detectors,
 such as local and global shapes and colors, 
 are combined to form an abstract \emph{feature vector}
 (see Fig.~\ref{fig-perceptual}).