[Gzz-commits] manuscripts/Paper buoyoing.mp paper.tex

[Tuomas J. Lukka]

CVSROOT:        /cvsroot/gzz
Module name:    manuscripts
Changes by:     Tuomas J. Lukka <address@hidden>        03/03/20 08:35:26

Modified files:
        Paper          : buoyoing.mp paper.tex 

Log message:
        Editing the description, unlocking

CVSWeb URLs:
http://savannah.gnu.org/cgi-bin/viewcvs/gzz/manuscripts/Paper/buoyoing.mp.diff?tr1=1.12&tr2=1.13&r1=text&r2=text
http://savannah.gnu.org/cgi-bin/viewcvs/gzz/manuscripts/Paper/paper.tex.diff?tr1=1.37&tr2=1.38&r1=text&r2=text

Patches:
Index: manuscripts/Paper/buoyoing.mp
diff -u manuscripts/Paper/buoyoing.mp:1.12 manuscripts/Paper/buoyoing.mp:1.13
--- manuscripts/Paper/buoyoing.mp:1.12  Wed Mar 19 10:20:42 2003
+++ manuscripts/Paper/buoyoing.mp       Thu Mar 20 08:35:26 2003
@@ -136,6 +136,9 @@
     (x-2*r,y)...(x,y-r)...(x+2*r,y)...(x,y+r)...cycle
 enddef;
 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
+%%%%% THE LINKS
+
 link("a", cir(50,30,25), "c", cir(70,160,37));
 link("b", cir(50,230,30), "a", cir(350,290,30));
 link("b", cir(60,170,35), "d", cir(150,60,50));
@@ -146,6 +149,8 @@
 
 pair p;
 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%%%% THE DOCUMENTS
 
 for i = -100 step 30 until 800 :
     p := (i,-50);
@@ -159,7 +164,7 @@
 \linewidth=18cm\begin{multicols}{5}\loremiii
 \end{multicols}}etex);
 
-for i = -100 step 40 until 800 :
+for i = -100 step 40 until 200 :
 for j = -100 step 40 until 800 :
     p := (i-.1*j,j+.05*i);
     fill p .. p+(20,5) .. p+(5,20) .. cycle;
Index: manuscripts/Paper/paper.tex
diff -u manuscripts/Paper/paper.tex:1.37 manuscripts/Paper/paper.tex:1.38
--- manuscripts/Paper/paper.tex:1.37    Thu Mar 20 08:34:11 2003
+++ manuscripts/Paper/paper.tex Thu Mar 20 08:35:26 2003
@@ -11,7 +11,7 @@
 
 \newif\ifpics
 \picsfalse
-%\picstrue
+\picstrue
 
 \title{Rendering recognizably unique textures}
 % Representing Identity 
@@ -49,6 +49,14 @@
 
 BLEACHING PIC + ZOOM EFFECT ON READABILITY
 
+MOTIVATING EXAMPLE NOT YET USER-TESTED
+
+IMAGE OF INVERTING THE VISUAL SYSTEM!!!
+
+IMAGE: RENDERING MODES, TEXTURE COORDINATES!
+
+SPARSE CODING: A TEXTURE CONTAINING BOTH YELLOW TRIANGLE AND
+RED SQUARE MIXES WITH ONE CONTAINING RED TRIANGLE AND YELLOW SQUARE
 
 TJL
 
@@ -414,18 +422,26 @@
 \includegraphics[width=\fw]{buoyoing.14}
 \includegraphics[width=\fkw]{buoyoing.15}
 \caption{
-An example of the structure used by BuoyOING.
-a) A small network of documents.
-b)..e) The animation seen when traversing the link from node F to H.
-In b) we are in the node F and see the relevant {\em fragment} of H,
-and in the c) and d) the view fluidly animates to the opposite case.
+The motivating example for unique backgrounds:
+a focus+context interface for browsing bidirectionally hyperlinked documents.
+The interface shows the relevant {\em fragments} of the other ends of the links
+and animates them fluidly to the focus upon traversing the link.,
+The (trivial) document network shown in a).
+The overall organization of the small network used as an example is shown in 
a).
+In b) and c) the same sequence of user's views to the network is shown, in b)
+without and in c) with background texture. 
+There are three keyframes where the view stops and two frames of each 
animation between the keyframes
+are shown.
+The unique backgrounds help the user notice that the upper right buoy in the 
last keyframe
+is actually a part of the same document (1) which was in the focus in the 
first keyframe.
+Our hypothesis is that this will aid user orientation.
 }
 \end{figure*}
 
 In the diagram above, the letters and colors helped identify the documents.
 Now, 
 
-\section{Unique Background Textures}
+\section{Generating Unique Background Textures}
 
 %XXX: shorten by one half column 
 
@@ -434,23 +450,19 @@
 XXX: simple models (filtering) can have good explanatory power
 on texture discrimination\cite{bergen88earlyvision}.
 
-TJL
+In this section, we discuss the methods to generate unique
+background textures on an abstract level.
 
-We define a unique background texture as an easily
-distinguishable and recognizable texture 
-that doesn't 
-significantly impair the reading of black text painted on top.
-In this section, we discuss 
-procedural generation
-of such textures
-from a seed number, e.g.,~the hash code of the identity of the object
-to be textured.
+To be useful, the unique backgrounds should be easily
+distinguishable and recognizable, and should not
+significantly impair the reading of black text on top of it.
 
 The ability to distinguish a particular texture from a large set
 depends on the distribution of textures in the set.
-It is intuitively clear that textures with independently
+For instance,
+it is intuitively clear that textures with independently
 random texel values would be a very bad choice: all such
-textures would look alike. 
+textures would look alike, being just noise. 
 In order to design a distinguishable distribution of textures,
 we have to take into account the properties of the human
 visual system.
@@ -471,7 +483,7 @@
 by forming contours and possibly
 other higher-level constructions. 
 These higher levels are not yet thoroughly understood;
-theories of structural object perception 
+some theories
 (see, e.g., Biederman\cite{biederman87})
 assume certain primitive shapes whose 
 structure facilitates recognition.
@@ -500,24 +512,29 @@
 % The basic assumption of the model is that an image
 % is perceived as a set of features 
 
-We make the assumption
+The simple model we use here assumes
 that at some point,
 the results from the  different feature detectors,
 such as local and global shapes and colors, 
 are combined to form an abstract \emph{feature vector}
 (see Fig.~\ref{fig-perceptual}).
-The feature vector is used to compute which concept the particular
-input corresponds to, in a simple perceptron-like 
+The feature vector is then used to compute 
+which concept the particular
+input corresponds to by comparing it to memorized models
+in a simple perceptron-like 
 fashion\cite{rosenblatt62neurodynamics,widrow60adaptive}.
-This configuration is sometimes used in neural computation.
+This configuration is commonly used in neural computation.
 
-The 
+This 
 rough, qualitative 
-model explains readily why uniformly random texels 
-would not make easily distinguishable patterns: different instances
-of noise would all yield almost 
-exactly the same feature vector in the brain.
-Noise has no global shape because there is no correlation between
+model is able to explain why uniformly random texels 
+do not make easily distinguishable background textures: 
+after the ``pre-processing'',
+different instances
+of noise would all yield 
+{\em almost 
+exactly the same feature vector} in the brain.
+Noise has no global shapes because there is no correlation between
 the random local features; it is simply perceived as the distribution
 of the local features, i.e., color and overall frequency 
 (the density of texels).
@@ -538,13 +555,7 @@
 % XXX: Why wouldn't it always be the same?
 % - seeing different parts of the texture?
 % - ambiguous perception?
-\item There should be as many possible features in the distribution
-    as possible. For example, if there were no yellow textures,
-    or if there were no curved lines, we would be wasting
-    recognition potential by leaving some elements
-    of the feature vector always zero.
-\item (Most abstractly) 
-    The entropy of the feature vectors
+\item The entropy of the feature vectors
     over the distribution of textures, should be maximized.
 \end{itemize}
 
@@ -567,15 +578,21 @@
 The last part means essentially
 that if all square-like shapes were green, we would again be
 wasting recognitive power.
-Indeed, entropy is maximized when the features are distributed
+There should also be as many possible features in the distribution
+    as possible. For example, if there were no yellow textures,
+    or if there were no curved lines, we would be wasting
+    recognition potential by leaving some elements
+    of the feature vector always zero.
+
+Indeed, the entropy is maximized when the features are distributed
 independently from each other:
 features orthogonal to human perception 
 (e.g.,~color, direction of fastest luminance change)
 should be independently random, and features not orthogonal 
 (e.g. colors of neighbouring pixels) 
-should be correlated so as to maximize the entropy 
-(e.g. pixels on a small area should correlate enough to
-facilitate perception of contours).
+should be correlated so as to maximize the entropy.
+For example, pixels on a small area should correlate enough to
+facilitate perception of contours.
 
 In a sense, the model of perception should be {\em inverted}
 in order to produce a unique background from 
@@ -613,8 +630,6 @@
 
 \section{Hardware-accelerated implementation}
 
-TJL
-
 In this section, we discuss our hardware-accelerated implementation
 (libpaper)
 of unique backgrounds (papers).
@@ -1275,7 +1290,7 @@
 using 2 passes as we currently do is too much; 
 it should be possible to obtain interesting textures with just one pass.
 We are also working on implementing
-these algorithms on ATI's extensions, due to their recent release
+these algorithms on OpenGL ARB extensions ..., due to their recent release
 of a Linux driver. 
 
 % However, we see the proprietary extensions only
@@ -1345,8 +1360,8 @@
 Benja Fallenstein,
 Matti Katila,
 and Asko Soukka
-have been involved in the development of other aspects of the BuoyOING 
-interface (not related to the background textures presented here).
+have contributed to the development of the BuoyOING 
+interface in aspects not related to the unique background textures.
 
 
 \bibliographystyle{plain}