[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 11:59:30

Modified files:
        Paper          : paper.tex 

Log message:
        reorg

CVSWeb URLs:
http://savannah.gnu.org/cgi-bin/viewcvs/gzz/manuscripts/Paper/paper.tex.diff?tr1=1.43&tr2=1.44&r1=text&r2=text

Patches:
Index: manuscripts/Paper/paper.tex
diff -u manuscripts/Paper/paper.tex:1.43 manuscripts/Paper/paper.tex:1.44
--- manuscripts/Paper/paper.tex:1.43    Thu Mar 20 10:56:47 2003
+++ manuscripts/Paper/paper.tex Thu Mar 20 11:59:29 2003
@@ -261,19 +261,79 @@
 using 
 other textures as a starting point 
 (see, e.g., Heeger\cite{heeger95pyramid}),
-and perceptually, for visualizing surface 
orientation\cite{schweitzer83texturing,interrante97illustrating} and scalar or 
vector fields\cite{ware95texture}.
-
-Statistical modeling of textures as samples from a probability 
-distribution on a random field as already seen in \cite{julesz62visualpattern}
-in a simple form.
-The most popualar computational approach is Markov random fields
-\cite{cross83markov,geman84stochastic}, where the value of each pixel
-depends only on the values of its neighborhood (local characteristics).
-XXX: resolution-dependency?
+perceptually, for visualizing surface 
orientation\cite{schweitzer83texturing,interrante97illustrating} and scalar or 
vector fields\cite{ware95texture},
+and statistically, as samples from a probability distribution on a random field
+\cite{cross83markov,geman84stochastic}.
+
+%Textures have also been modeled statistically, 
+%as samples from a probability distribution on a random field.
+%The most popualar computational approach is Markov random fields
+%\cite{cross83markov,geman84stochastic}, 
+%where the value of each pixel
+%depends only on the values of its neighborhood (local characteristics).
+%XXX: resolution-dependency?
 
 % In this article, we apply texture shading to synthesize a large number
 % of unique textures for distinguishing virtual objects.
 
+\subsection{Texture perception}
+
+Psychophysical studies on texture perception have mostly concentrated
+on pre-attentive 
+\emph{visual texture discrimination}\cite{julesz62visualpattern}, 
+the ability of human observers to effortlessly discriminate
+pairs of certain 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.
+%
+First discrimination models were 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).
+However, the order of similarity in the statistics did not 
+consistently explain discrimination performance, and certain
+distinctive local features were conjectured.
+
+Julesz\cite{julesz81textons} proposed that texture discrimination could be 
+explained by the densities of textons, fundamental texture elements, such as
+elongated blobs, line terminators, and line crossings. 
+However, the textons are hard to define formally.
+
+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).
+%In \cite{heeger95pyramid}, new textures with appearance similar
+%to a given texture are created by matching certain histograms 
+%of filter responses.
+
+%XXX: texture perception reviews
+
+There have been studies on 
+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, 
+three dimensions have also been
+sufficient \cite{rao96texturenaming}, 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
+
 \subsection{Focus+Context views}
 
 Focus+Context, or, fisheye views\cite{fc-fisheye} are 
@@ -1121,73 +1181,16 @@
 we start from a fairly light background (>80 XXX).
 On a darker background, this approach would probably look silly.
 
-\subsection{Texture perception}
-
-Psychophysical studies on texture perception have mostly concentrated
-on \emph{visual texture discrimination}\cite{julesz62visualpattern}, 
-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.
-%
-First discrimination models were 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).
-However, the order of similarity in the statistics did not 
-consistently explain discrimination performance, and certain
-distinctive local features were conjectured.
-
-Julesz\cite{julesz81textons} proposed that discrimination could be explained
-by the densities of textons, fundamental texture elements, such as
-elongated blobs, line terminators, and line crossings. 
-However, the textons are hard to define formally.
-
-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).
-%In \cite{heeger95pyramid}, new textures with appearance similar
-%to a given texture are created by matching certain histograms 
-%of filter responses.
-
-%XXX: texture perception reviews
-
-In our application visual texture discrimination is not as
-much of an issue as memorizability and recognizability of
-previously seen textures, because the textures are ... discriminable..
-
-
-There have been studies on 
-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
-
 \subsection{Recognizability and memorizability}
 
-The textures created by our algorithm, 
-although repeating, are more like complete images 
-than the statistical microstructure studied in most texture perception work. 
-Therefore, we also need to consider the higher level processes of vision.
+%In our application visual texture discrimination is not as
+%much of an issue as memorizability and recognizability of
+%previously seen textures, because the textures are ... discriminable..
+
+%The textures created by our algorithm, 
+%although repeating, are more like complete images 
+%than the statistical microstructure studied in most texture perception work. 
+%Therefore, we also need to consider the higher level processes of vision.
 
 Experiments on black-and-white %(faces,) 
 ink blots and snow crystals