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\subsection{Texture perception} |
\subsection{Texture perception} |
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Studies on texture perception has mostly concentrated on |
Studies on texture perception have mostly concentrated on |
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texture discriminability. |
texture discrimination, |
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First experiments on computer-generated, unnatural textures |
First experiments on computer-generated, unnatural textures |
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in the 60s (see, e.g, \cite{julesz62visualpattern}) lead to |
in the 60s (see, e.g, \cite{julesz62visualpattern}) led to |
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proposals of texture discrimation models based on |
proposals of texture discrimation models based on |
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$N$th order statistics of pixels and elaborate connectivity |
$N$th order statistics of pixels and elaborate connectivity |
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structures of certain micropatterns.XXX |
structures of certain micropatterns.XXX |
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Of cource, such models are not directly applicable on high-resolution |
Textons \cite{julesz81textons}: elongated blobs, line terminators, |
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line crossings, etc. |
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Of course, such models are not directly applicable on high-resolution |
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textures; some kind of filtering would be required to obtain the input. |
textures; some kind of filtering would be required to obtain the input. |
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However, the filtering itself can also have good explanatory power |
However, the filtering itself can also have good explanatory power |
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\cite{bergen88earlyvision}.XXX |
\cite{bergen88earlyvision}.XXX |
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Filtering based approach, e.g., \cite{heeger95pyramid}. |
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Statistical modeling of textures as samples from a probability |
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distribution on a random field as already seen in \cite{julesz62visualpattern} |
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in a very simple form. |
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The most popualar approach is Markov random fields |
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\cite{cross83markov, geman84stochastic}, where a texture |
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is characterized by its local statistics. |
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\subsection{Focus+Context views} |
\subsection{Focus+Context views} |
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Focus+Context, or, fisheye views\cite{fc-fisheye} are |
Focus+Context, or, fisheye views\cite{fc-fisheye} are |