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revision 1.16 by jvk, Sat Mar 8 13:50:47 2003 UTC revision 1.17 by jvk, Sat Mar 8 15:13:33 2003 UTC
# Line 247  surface shape\cite{interrante97illustrat Line 247  surface shape\cite{interrante97illustrat
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
# Line 279  However, the filtering itself can also h Line 278  However, the filtering itself can also h
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

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