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University of Jyv\"askyl\"a, PO.~Box~35\\ |
University of Jyv\"askyl\"a, PO.~Box~35\\ |
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FIN-40351~Jyv\"askyl\"a\\ |
FIN-40351~Jyv\"askyl\"a\\ |
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Finland\\ |
Finland\\ |
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lukka@iki.fi,\ \ jvk@iki.fi |
jvk@iki.fi,\ \ lukka@iki.fi |
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} |
} |
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\begin{document} |
\begin{document} |
244 |
and perceptually, for visualizing surface orientation\cite{schweitzer83texturing}, scalar or vector fields\cite{ware95texture}, and |
and perceptually, for visualizing surface orientation\cite{schweitzer83texturing}, scalar or vector fields\cite{ware95texture}, and |
245 |
surface shape\cite{interrante97illustrating}. |
surface shape\cite{interrante97illustrating}. |
246 |
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% In this article, we apply texture shading to synthesize a large number |
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% of unique textures for distinguishing virtual objects. |
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\subsection{Texture perception} |
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Psychological studies on texture perception have mostly concentrated |
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on \emph{texture discrimination}, the ability of human observers to |
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discriminate pairs of textures. |
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The term is often used interchangably with \emph{texture segregation}, |
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the more specific task of finding the border between differently textured |
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areas (different phases of local characteristics at the |
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border can segregate otherwise indiscriminable textures). |
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|
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|
First experiments on computer-generated, unnatural textures in the 60s |
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\cite{julesz62visualpattern} led to proposals of discrimination models |
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based on the $N$th-order statistics of textures |
|
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(the joint distributions of the values at the corners of a randomly |
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placed (translated) $N$-gon for all different $N$-gons). |
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%and connectivity structures of certain micropatterns. |
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|
247 |
Statistical modeling of textures as samples from a probability |
Statistical modeling of textures as samples from a probability |
248 |
distribution on a random field as already seen in \cite{julesz62visualpattern} |
distribution on a random field as already seen in \cite{julesz62visualpattern} |
249 |
in a simple form. |
in a simple form. |
252 |
depends only on the values of its neighborhood (local characteristics). |
depends only on the values of its neighborhood (local characteristics). |
253 |
XXX: resolution-dependency? |
XXX: resolution-dependency? |
254 |
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|
255 |
Attempt to explain texture discrimination by the densities of textons |
% In this article, we apply texture shading to synthesize a large number |
256 |
\cite{julesz81textons}, fundamental texture elements, such as |
% of unique textures for distinguishing virtual objects. |
|
elongated blobs, line terminators, line crossings, etc. |
|
|
However, the textons are hard to define formally. |
|
|
|
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|
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 |
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XXX: reviews |
|
|
|
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|
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}) |
|
257 |
|
|
258 |
\subsection{Focus+Context views} |
\subsection{Focus+Context views} |
259 |
|
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410 |
|
|
411 |
\section{Unique Background Textures} |
\section{Unique Background Textures} |
412 |
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413 |
|
%XXX: shorten by one half column |
414 |
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|
415 |
XXX: - visual discrimination experiments: \cite{julesz62visualpattern} |
XXX: - visual discrimination experiments: \cite{julesz62visualpattern} |
416 |
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|
417 |
XXX: simple models (filtering) can have good explanatory power |
XXX: simple models (filtering) can have good explanatory power |
1048 |
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|
1049 |
\subsection{Recognizability and memorizability} |
\subsection{Recognizability and memorizability} |
1050 |
|
|
1051 |
|
% XXX: shorten by half |
1052 |
|
|
1053 |
JVK |
JVK |
1054 |
|
|
1060 |
Furthermore, our textures are on a higher level, |
Furthermore, our textures are on a higher level, |
1061 |
more like complete pictures than the usually studied microstructure. |
more like complete pictures than the usually studied microstructure. |
1062 |
|
|
1063 |
|
|
1064 |
|
XXX |
1065 |
|
|
1066 |
|
%\subsection{Texture perception} |
1067 |
|
|
1068 |
|
Psychophysical studies on texture perception have mostly concentrated |
1069 |
|
on \emph{texture discrimination}, the ability of human observers to |
1070 |
|
discriminate pairs of textures. |
1071 |
|
The term is often used interchangably with \emph{texture segregation}, |
1072 |
|
the more specific task of finding the border between differently textured |
1073 |
|
areas (different phases of local characteristics at the |
1074 |
|
border can segregate otherwise indiscriminable textures). |
1075 |
|
|
1076 |
|
First experiments on computer-generated, unnatural textures in the 60s |
1077 |
|
\cite{julesz62visualpattern} led to proposals of discrimination models |
1078 |
|
based on the $N$th-order statistics of textures |
1079 |
|
(the joint distributions of the values at the corners of a randomly |
1080 |
|
placed (translated) $N$-gon for all different $N$-gons). |
1081 |
|
%and connectivity structures of certain micropatterns. |
1082 |
|
|
1083 |
|
Attempt to explain texture discrimination by the densities of textons |
1084 |
|
\cite{julesz81textons}, fundamental texture elements, such as |
1085 |
|
elongated blobs, line terminators, line crossings, etc. |
1086 |
|
However, the textons are hard to define formally. |
1087 |
|
|
1088 |
|
Much simpler filtering-based models can explain texture discrimination |
1089 |
|
just as well \cite{bergen88earlyvision}. |
1090 |
|
Essentially a bank of linear filters is applied to the texture followed |
1091 |
|
by a nonlinearity and then another set of filters. |
1092 |
|
In \cite{heeger95pyramid}, new textures with appearance similar |
1093 |
|
to a given texture are created by matching certain histograms |
1094 |
|
of filter responses. |
1095 |
|
|
1096 |
|
Mapping texture appearance to an Euclidian texture space |
1097 |
|
(see \cite{gurnsey01texturespace} and the references therein): |
1098 |
|
in the reported experiments, three dimensions have been sufficient |
1099 |
|
to explain most of the variation in the similarity judgements for |
1100 |
|
artificial textures. |
1101 |
|
However, the texture stimuli have been somewhat simple |
1102 |
|
(no color, lack of frequency-band interaction, etc.). |
1103 |
|
For some natural texture sets (see, e.g., \cite{rao96texturenaming}), |
1104 |
|
three dimensions have also been |
1105 |
|
sufficient, but often semantic connections cause the |
1106 |
|
similarity to be context-dependant, making it hard to assess the |
1107 |
|
dimensionality. |
1108 |
|
% XXX: this is something we should experiment with our textures |
1109 |
|
|
1110 |
|
XXX: reviews |
1111 |
|
|
1112 |
|
XXX: physiological knowledge of visual perception |
1113 |
|
(see, e.g.,~Bruce et al\cite{bruce96visualperception}); |
1114 |
|
|
1115 |
|
XXX: in most work, texture is considered as the output of a stochastic |
1116 |
|
process that produces certain repeating features. |
1117 |
|
Different samples from the process are considered as the same texture. |
1118 |
|
The textures created by our algorithm, although repeating, are more like |
1119 |
|
complete images rather than microstructure. |
1120 |
|
Therefore, higher level processes of vision are also involved |
1121 |
|
in the perception and recognition. |
1122 |
|
|
1123 |
|
theories of structural object perception |
1124 |
|
(see, e.g., Biederman\cite{biederman87}) |
1125 |
|
|
1126 |
|
|
1127 |
|
XXX |
1128 |
|
|
1129 |
Experiments on black-and-white %(faces,) |
Experiments on black-and-white %(faces,) |
1130 |
ink blots, and snow crystals |
ink blots, and snow crystals |
1131 |
\cite{goldstein71visualrecognition} show that |
\cite{goldstein71visualrecognition} show that |