[Gzz-commits] manuscripts/Paper paper.tex

[Janne V. Kujala]

CVSROOT:        /cvsroot/gzz
Module name:    manuscripts
Changes by:     Janne V. Kujala <address@hidden>        03/03/12 10:25:15

Modified files:
        Paper          : paper.tex 

Log message:
        notes

CVSWeb URLs:
http://savannah.gnu.org/cgi-bin/viewcvs/gzz/manuscripts/Paper/paper.tex.diff?tr1=1.19&tr2=1.20&r1=text&r2=text

Patches:
Index: manuscripts/Paper/paper.tex
diff -u manuscripts/Paper/paper.tex:1.19 manuscripts/Paper/paper.tex:1.20
--- manuscripts/Paper/paper.tex:1.19    Wed Mar 12 04:16:28 2003
+++ manuscripts/Paper/paper.tex Wed Mar 12 10:25:15 2003
@@ -251,15 +251,16 @@
 on \emph{texture discrimination}, the ability of human observers to
 discriminate pairs of textures.  
 The term is often used interchangably with \emph{texture segregation},
-the more specific task of finding the border between areas of
-different textures (different phases of local characteristics at the
+the more specific task of finding the border between differently textured 
+areas (different phases of local characteristics at the
 border can segregate otherwise indiscriminable textures).
 
 First experiments on computer-generated, unnatural textures in the 60s
 \cite{julesz62visualpattern} led to proposals of discrimination models
-based on $N$th-order statistics (the joint distributions of all
-$N$-tuples of pixels for given $N$) and connectivity structures of
-certain micropatterns.
+based on the $N$th-order statistics of textures 
+(the joint distributions of the values at the corners of a randomly
+placed (translated) $N$-gon for all different $N$-gons).
+%and connectivity structures of certain micropatterns.
 
 Statistical modeling of textures as samples from a probability 
 distribution on a random field as already seen in \cite{julesz62visualpattern}
@@ -269,19 +270,18 @@
 depends only on the values of its neighborhood (local characteristics).
 XXX: resolution-dependency?
 
-Attempt to explain texture perception by the densities of textons
+Attempt to explain texture discrimination by the densities of textons
 \cite{julesz81textons}, fundamental texture elements, such as
 elongated blobs, line terminators, line crossings, etc.  
 However, the textons are hard to define formally.
 
-Of course, such models are not directly applicable on high-resolution
-textures; some kind of filtering would be required to obtain the input.  
-However, the filtering itself can also have good explanatory power
-\cite{bergen88earlyvision}.XXX
-
-Filtering based approach, e.g., \cite{heeger95pyramid}.
+Much simpler filtering-based models can explain texture discrimination
+just as well \cite{bergen88earlyvision}.
 Essentially a bank of linear filters is applied to the texture followed
 by a nonlinearity and then another set of filters.
+In \cite{heeger95pyramid}, new textures with appearance similar
+to a given texture are created by matching certain histograms 
+of filter responses.
 
 Mapping texture appearance to an Euclidian texture space
 (see \cite{gurnsey01texturespace} and the references therein):
@@ -292,7 +292,7 @@
 (no color, lack of frequency-band interaction, etc.).
 For some natural texture sets (see, e.g., \cite{rao96texturenaming}), 
 three dimensions have also been
-sufficient, but often the semantic connections cause the
+sufficient, but often semantic connections cause the
 similarity to be context-dependant, making it hard to assess the 
 dimensionality.
 % XXX: this is something we should experiment with our textures
@@ -300,6 +300,14 @@
 XXX: reviews
 
 XXX: physiological knowledge of visual perception
+
+XXX: in most work, texture is considered as the output of a stochastic
+process that produces certain repeating features. 
+Different samples from the process are considered as the same texture.
+The textures created by our algorithm, although repeating, are more like 
+complete images rather than microstructure. 
+Therefore, higher level processes of vision are also involved
+in the perception and recognition.
 
 \subsection{Focus+Context views}