[Gzz-commits] gzz/Documentation/Manuscripts/Paper paper.tex

[Janne V. Kujala]

CVSROOT:        /cvsroot/gzz
Module name:    gzz
Changes by:     Janne V. Kujala <address@hidden>        02/11/20 08:18:15

Modified files:
        Documentation/Manuscripts/Paper: paper.tex 

Log message:
        more of the model

CVSWeb URLs:
http://savannah.gnu.org/cgi-bin/viewcvs/gzz/gzz/Documentation/Manuscripts/Paper/paper.tex.diff?tr1=1.47&tr2=1.48&r1=text&r2=text

Patches:
Index: gzz/Documentation/Manuscripts/Paper/paper.tex
diff -u gzz/Documentation/Manuscripts/Paper/paper.tex:1.47 
gzz/Documentation/Manuscripts/Paper/paper.tex:1.48
--- gzz/Documentation/Manuscripts/Paper/paper.tex:1.47  Wed Nov 20 06:06:15 2002
+++ gzz/Documentation/Manuscripts/Paper/paper.tex       Wed Nov 20 08:18:14 2002
@@ -194,27 +194,47 @@
 
 \section{Unique Textures}
 
-All PDF files look visually similar.
-It is easier to navigate if different textures are used as the background
-of a PDF document.
-
-context - fragment of PDF, location to which connected
-
-generating textures based on seed numbers [identity]
+...
+When the view is focused on one part of a document,
+context is provided by the fragments of documents, which the
+part in focus is connected to.
+It is hard to maintain orientation because 
+all PDF files look visually similar.
+
+It is easier to navigate if different textures are used 
+as the backgrounds of different documents.
+Unique textures provide instant cues on the
+identity of the focused and connected documents and
+a more prominent target for tracking movement between views.
+
+The identity is used as a seed for randomly choosing
+an easily distinguishable unique texture from a
+distribution based on a qualitative model of visual perception.
+%providing an infinite source of unique textures.
+%generating textures based on seed numbers [identity]
+The basic assumption of the model is that an image
+is perceived as a set of features.
+
+Current knowledge of visual perception \cite{bruce96visualperception}
+explains early visual processing very accurately.
+In visual cortex, there are cells sensitive to different 
+frequencies, orientations, and locations in the visual field.
+A good mathematical model for the sensitivity of the receptive fields is
+Gabor function, i.e., a two-dimensional Gaussian-modulated sinusoid.
+On a higher level, correlating local feature are combined 
+to global perception. 
+For example, contours are formed from consistent directions 
+of adjacent receptive fields.
+
+The higher levels of visual processing are no longer hierarchical
+and not throughly understood.
+We simply assume that the intensities of different features,
+such as local and global shapes and colors, form a \emph{feature vector},
+which facilitates recognition and memorization of images.
 
 distinguishability: should produce random vector in brain 
     (perception model in Fig.~\ref{fig-perceptual}) -- saving of bits
 
-The model:\\
-- visual cortex: image converted to Gabor-function basis (receptive fields)
-  \cite{bruce96visualperception} \\
-- higher levels: correlating local features combined to global perception, 
-  e.g. contours formed from consistent directions of adjacent receptive fields.
-  \\ 
-- feature detectors: different colors and certain shapes are recognized \\
-
-The ``intensities'' of the features produce a \emph{feature vector}.
-
 In \cite{schweitzer83texturing}, simple local features in artificial
 textures are used for implying global surface shape.
 
@@ -232,9 +252,9 @@
 would not make easily distinguishable patterns: different instances
 of noise would all yield almost exactly the same 
 % pattern at the XXX
-feature vector:
+feature vector in brain:
 Noise has no global shape because there is no correlation between
-the random local features, it is simply perceived as the distribution
+the random local features; it is simply perceived as the distribution
 of the local features (color and overall frequency (the size of texels)).
 
 Features independent at XXX should not correlate between XXX; for example,