[Gzz-commits] manuscripts/Paper paper.tex

[Tuomas J. Lukka]

CVSROOT:        /cvsroot/gzz
Module name:    manuscripts
Changes by:     Tuomas J. Lukka <address@hidden>        03/03/30 23:36:52

Modified files:
        Paper          : paper.tex 

Log message:
        Writing the sections

CVSWeb URLs:
http://savannah.gnu.org/cgi-bin/viewcvs/gzz/manuscripts/Paper/paper.tex.diff?tr1=1.74&tr2=1.75&r1=text&r2=text

Patches:
Index: manuscripts/Paper/paper.tex
diff -u manuscripts/Paper/paper.tex:1.74 manuscripts/Paper/paper.tex:1.75
--- manuscripts/Paper/paper.tex:1.74    Sun Mar 30 15:09:40 2003
+++ manuscripts/Paper/paper.tex Sun Mar 30 23:36:52 2003
@@ -59,48 +59,55 @@
 
 TJL
 
-We apply a rough, qualitative model of visual perception
-to motivate 
-general principles for designing recognizably unique textures
-for use as backgrounds for data.
+We present a perceptually designed hardware-accelerated
+algorithm for generating unique background textures for data.
 To be recongizable,
 the texture should produce a random feature vector in the brain
-{\em after} visual feature extraction. 
-
-Our motivating example is...
-
-We show how object identity can be visualized
-by
-unique background textures 
-procedurally generated from the identity (e.g., hashcode) of the document or 
data item.
-
-XXXWAFFLE Unique backgrounds can assist
-user orientation
-when browsing a set of objects with
-similar overall appearance, 
-especially in focus+context views.
+after visual feature extraction. 
 
+Our motivating example is the BuoyOING user interface for
+browsing hyperlinked document sets with fluid, non-disruptive linking.
+The interface shows a fragment of the target document of a link in the 
marginal,
+which, upon traversing the link, expands to fill the screen.
+Our goal is to avoid user disorientation by
+texturing each document with a unique background so that the originating
+document can easily be recognized from the fragment.
+The user should then be able to learn the textures of the 
+most often visited
+documents, as per Zipf's law.
+
+%We show how object identity can be visualized
+%by
+%unique background textures 
+%procedurally generated from the identity (e.g., hashcode) of the document or 
data item.
+
+%XXXWAFFLE Unique backgrounds can assist
+%user orientation
+%when browsing a set of objects with
+%similar overall appearance, 
+%especially in focus+context views.
+%
 %Unique backgrounds could be useful for assisting user orientation
 %in several different user interfaces; our primary application
 %is navigating hyperstructures using Focus+Context views.
 %
 %We can rapidly generate a texture for any document the user visits, 
-%and the user will be able to learn the textures of the 
-%most often visited
-%documents, as per Zipf's law.
+%and 
+
 %
 %In Focus+Context views, the textures can act as visual cues in the context
 %(information foraging).
 
 We discuss our freely available hardware-accelerated implementation 
 of unique backgrounds
-on the NV10 and NV25 architectures, and
-show an example user interface for browsing linked PDF documents
-in a focus+context view using unique backgrounds.
+on the NV10 and NV25 architectures. 
+
+We show the results of an initial experiment ... XXX
 
 Text readability is a major concern for using such backgrounds,
-and we discuss a method for enhancing readability by unnoticeably
-bleaching the background around text..
+and we discuss a method for enhancing readability by both providing 
+fast, interactive zooming and 
+unnoticeably bleaching the background around text.
 
 % The implementation works by combining a small set of basis textures
 % and perceptually chosen colors
@@ -177,21 +184,13 @@
 % data identity 
 % to make
 % similar but distinct objects distinguishable and easily recognizable.
-
-Discrete, unordered variables often occur coupled to other
-variables when drawing graphs; for example, a variable representing
-different makes of cars would be such. 
-If the number of different values that the variable takes are few,
-distinct symbols can be used...
-
-Visualizing a discrete variable with unordered values
-
 In this article, we introduce the use of procedurally generated unique 
backgrounds
-as a visualization of data identity: if each data item with a different 
identity has 
-a different, easily distinguishable texture, the user can become aware of the 
identity
+as a visualization of document identity: if each document has 
+a different, easily distinguishable background texture, the user can become 
aware of the identity
 of a displayed item at a glance, without explicitly reading the title.
-Even more importantly, the user can become aware of the identity just by 
seeing 
-any {\em fragment} of the item, instead of the ``title page''.
+The user can even become aware of the identity just by seeing 
+any {\em fragment} of the item, instead of the title page. This property is 
vital
+for our example application discussed in Section~\ref{secbuoyoing}.
 
 
 % as a navigation aid in focus+context views.
@@ -212,22 +211,19 @@
 % a more prominent target for tracking movement between views.
 
 In the following sections,
-we first review related work on texturing, 
-Focus+Context views.
+we first review related work on texturing.
 Next, we discuss the motivating example for this work:
-a specific (xupdf XXX) focus+context
+the BuoyOING focus+context
 user interface to a hypertext structure.
 Then, we formulate general principles for designing
 recognizable backgrounds and present a hardware-accelerated implementation.
 Following this, we discuss enhancing text readability on such backgrounds
 and practical experiences.
-Finally, we show an example application of unique backgrounds
-for browsing linked PDF documents in a focus+context view.
+%Finally, we show an example application of unique backgrounds
+%for browsing linked PDF documents in a focus+context view.
 
 \section{Related work}
 
-\subsection{Texturing}
-
 The {\em texture} of a surface, taken literally, is its translation-invariant 
statistical microstructure.
 In computer graphics,
 the word {\em texturing} is used in 
@@ -334,18 +330,6 @@
 %dimensionality.
 %% XXX: this is something we should experiment with our textures
 
-\subsection{Focus+Context views}
-
-Focus+Context, or, fisheye views\cite{fc-fisheye} are 
-a paradigm for viewing large, 
-structured information sets 
-by showing the current area of 
-interest (focus) magnified
-and the structurally connected but further-away 
-elements peripherally, with less magnification.
-Much of the work on focus+context views has
-concentrated on tree structures\cite{lamping96hyperbolic,fc-images},
-or flat 2D images or maps\cite{fc-taxonomy}.
 
 % The type of focus+context view for whi
 
@@ -439,6 +423,19 @@
 
 \section{The motivation for Unique Backgrounds: the BuoyOING user interface}
 
+\label{secbuoyoing}
+
+Focus+Context, or, fisheye views\cite{fc-fisheye} are 
+a paradigm for viewing large, 
+structured information sets 
+by showing the current area of 
+interest (focus) magnified
+and the structurally connected but further-away 
+elements peripherally, with less magnification.
+Much of the work on focus+context views has
+concentrated on tree structures\cite{lamping96hyperbolic,fc-images},
+or flat 2D images or maps\cite{fc-taxonomy}.
+
 TJL
 
 The motivating example for 
@@ -1292,13 +1289,14 @@
 \fi
 \caption{
 \label{fig-zipf}
-Zipf's law concretized: why remembering 15 textures helps.
+Zipf's law concretized: why distinguishing 15 
+textures from a large number of others helps.
 In real life, accesses to documents often follow Zipf's law, meaning that
 some documents get accessed far more often than most.
 Each square represents a document, and the area of each square is scaled
 to its rate of accesses.
 The diagram shows 2000 documents weighted with Zipf's law with exponent 1.1.
-The 15 most important documents account for 50\% of the accesses.
+Here, the 15 most important documents account for approximately half of the 
accesses.
 % 0.50469672124463749
 }
 \end{figure}
@@ -1521,6 +1519,15 @@
 % \item Papertest, showing many images of papers, zooming, ...
 % \end{itemize}
 % 
+
+
+Discrete, unordered variables often occur coupled to other
+variables when drawing graphs; for example, a variable representing
+different makes of cars would be such. 
+If the number of different values that the variable takes are few,
+distinct symbols can be used...
+
+Visualizing a discrete variable with unordered values
 
 
 \section{Acknowledgments}