[Gzz-commits] manuscripts/Paper paper.tex

[Janne V. Kujala]

CVSROOT:        /cvsroot/gzz
Module name:    manuscripts
Changes by:     Janne V. Kujala <address@hidden>        03/03/18 09:06:48

Modified files:
        Paper          : paper.tex 

Log message:
        reorg

CVSWeb URLs:
http://savannah.gnu.org/cgi-bin/viewcvs/gzz/manuscripts/Paper/paper.tex.diff?tr1=1.30&tr2=1.31&r1=text&r2=text

Patches:
Index: manuscripts/Paper/paper.tex
diff -u manuscripts/Paper/paper.tex:1.30 manuscripts/Paper/paper.tex:1.31
--- manuscripts/Paper/paper.tex:1.30    Tue Mar 18 04:23:34 2003
+++ manuscripts/Paper/paper.tex Tue Mar 18 09:06:47 2003
@@ -26,7 +26,7 @@
   University of Jyv\"askyl\"a, PO.~Box~35\\
   FIN-40351~Jyv\"askyl\"a\\
   Finland\\
-  address@hidden,\ \  address@hidden
+  address@hidden,\ \  address@hidden
 }
 
 \begin{document}
@@ -244,26 +244,6 @@
 and perceptually, for visualizing surface 
orientation\cite{schweitzer83texturing}, scalar or vector 
fields\cite{ware95texture}, and
 surface shape\cite{interrante97illustrating}.
 
-% In this article, we apply texture shading to synthesize a large number
-% of unique textures for distinguishing virtual objects.
-
-\subsection{Texture perception}
-
-Psychological studies on texture perception have mostly concentrated
-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 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 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}
 in a simple form.
@@ -272,48 +252,8 @@
 depends only on the values of its neighborhood (local characteristics).
 XXX: resolution-dependency?
 
-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.
-
-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):
-in the reported experiments, three dimensions have been sufficient
-to explain most of the variation in the similarity judgements for
-artificial textures. 
-However, the texture stimuli have been somewhat simple 
-(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 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
-
-XXX: reviews
-
-XXX: physiological knowledge of visual perception
-(see, e.g.,~Bruce et al\cite{bruce96visualperception});
-
-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.
-
-theories of structural object perception 
-(see, e.g., Biederman\cite{biederman87})
+% In this article, we apply texture shading to synthesize a large number
+% of unique textures for distinguishing virtual objects.
 
 \subsection{Focus+Context views}
 
@@ -470,6 +410,8 @@
 
 \section{Unique Background Textures}
 
+%XXX: shorten by one half column 
+
 XXX: - visual discrimination experiments: \cite{julesz62visualpattern}
 
 XXX: simple models (filtering) can have good explanatory power
@@ -1106,6 +1048,7 @@
 
 \subsection{Recognizability and memorizability}
 
+% XXX: shorten by half
 
 JVK
 
@@ -1116,6 +1059,72 @@
 previously seen textures.
 Furthermore, our textures are on a higher level,
 more like complete pictures than the usually studied microstructure.
+
+
+XXX
+
+%\subsection{Texture perception}
+
+Psychophysical studies on texture perception have mostly concentrated
+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 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 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.
+
+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.
+
+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):
+in the reported experiments, three dimensions have been sufficient
+to explain most of the variation in the similarity judgements for
+artificial textures. 
+However, the texture stimuli have been somewhat simple 
+(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 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
+
+XXX: reviews
+
+XXX: physiological knowledge of visual perception
+(see, e.g.,~Bruce et al\cite{bruce96visualperception});
+
+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.
+
+theories of structural object perception 
+(see, e.g., Biederman\cite{biederman87})
+
+
+XXX
 
 Experiments on black-and-white %(faces,) 
 ink blots, and snow crystals