247 |
|
|
248 |
\subsection{Texture perception} |
\subsection{Texture perception} |
249 |
|
|
250 |
Studies on texture perception have mostly concentrated on |
Psychological studies on texture perception have mostly concentrated |
251 |
texture discrimination, |
on texture discrimination, the ability of human observers to discriminate |
252 |
|
pairs of textures. |
253 |
|
% XXX: segregation vs. discrimination |
254 |
|
|
255 |
First experiments on computer-generated, unnatural textures |
First experiments on computer-generated, unnatural textures |
256 |
in the 60s (see, e.g, \cite{julesz62visualpattern}) led to |
in the 60s \cite{julesz62visualpattern} led to |
257 |
proposals of texture discrimation models based on |
proposals of discrimination models based on |
258 |
$N$th order statistics of pixels and elaborate connectivity |
$N$th order statistics of pixels and connectivity |
259 |
structures of certain micropatterns.XXX |
structures of certain micropatterns. |
260 |
|
|
261 |
Statistical modeling of textures as samples from a probability |
Statistical modeling of textures as samples from a probability |
262 |
distribution on a random field as already seen in \cite{julesz62visualpattern} |
distribution on a random field as already seen in \cite{julesz62visualpattern} |
263 |
in a very simple form. |
in a simple form. |
264 |
The most popualar computational approach is Markov random fields |
The most popualar computational approach is Markov random fields |
265 |
\cite{cross83markov, geman84stochastic}, where a texture |
\cite{cross83markov, geman84stochastic}, where a texture |
266 |
is characterized by its local statistics. |
is characterized by its local statistics. |
267 |
However, these approaches are often not feasible on large |
XXX: resolution-dependency? |
|
large neighborhoods, but essentially work on pixel scale. |
|
268 |
|
|
269 |
Attempt to explain texture perception by the densities of textons |
Attempt to explain texture perception by the densities of textons |
270 |
\cite{julesz81textons}, fundamental texture elements, such as |
\cite{julesz81textons}, fundamental texture elements, such as |
271 |
elongated blobs, line terminators, line crossings, etc. |
elongated blobs, line terminators, line crossings, etc. |
272 |
However, the textons are hard to define formally. |
However, the textons are hard to define formally. |
273 |
|
|
|
Textures are continuous |
|
|
|
|
274 |
Of course, such models are not directly applicable on high-resolution |
Of course, such models are not directly applicable on high-resolution |
275 |
textures; some kind of filtering would be required to obtain the input. |
textures; some kind of filtering would be required to obtain the input. |
276 |
However, the filtering itself can also have good explanatory power |
However, the filtering itself can also have good explanatory power |
278 |
|
|
279 |
Filtering based approach, e.g., \cite{heeger95pyramid}. |
Filtering based approach, e.g., \cite{heeger95pyramid}. |
280 |
Essentially a bank of linear filters is applied to the texture followed |
Essentially a bank of linear filters is applied to the texture followed |
281 |
by a nonlineary and then another set of filters. |
by a nonlinearity and then another set of filters. |
282 |
|
|
283 |
XXX: reviews |
XXX: reviews |
284 |
|
|
285 |
|
XXX: physiological knowledge of visual perception |
286 |
|
|
287 |
\subsection{Focus+Context views} |
\subsection{Focus+Context views} |
288 |
|
|
289 |
Focus+Context, or, fisheye views\cite{fc-fisheye} are |
Focus+Context, or, fisheye views\cite{fc-fisheye} are |