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TJL |
TJL |
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We apply a rough, qualitative model of visual perception |
We present a perceptually designed hardware-accelerated |
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to motivate |
algorithm for generating unique background textures for data. |
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general principles for designing recognizably unique textures |
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for use as backgrounds for data. |
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To be recongizable, |
To be recongizable, |
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the texture should produce a random feature vector in the brain |
the texture should produce a random feature vector in the brain |
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{\em after} visual feature extraction. |
after visual feature extraction. |
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Our motivating example is... |
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We show how object identity can be visualized |
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by |
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unique background textures |
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procedurally generated from the identity (e.g., hashcode) of the document or data item. |
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XXXWAFFLE Unique backgrounds can assist |
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user orientation |
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when browsing a set of objects with |
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similar overall appearance, |
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especially in focus+context views. |
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Our motivating example is the BuoyOING user interface for |
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browsing hyperlinked document sets with fluid, non-disruptive linking. |
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The interface shows a fragment of the target document of a link in the marginal, |
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which, upon traversing the link, expands to fill the screen. |
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Our goal is to avoid user disorientation by |
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texturing each document with a unique background so that the originating |
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document can easily be recognized from the fragment. |
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The user should then be able to learn the textures of the |
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most often visited |
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documents, as per Zipf's law. |
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%We show how object identity can be visualized |
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%by |
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%unique background textures |
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%procedurally generated from the identity (e.g., hashcode) of the document or data item. |
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%XXXWAFFLE Unique backgrounds can assist |
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%user orientation |
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%when browsing a set of objects with |
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%similar overall appearance, |
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%especially in focus+context views. |
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% |
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%Unique backgrounds could be useful for assisting user orientation |
%Unique backgrounds could be useful for assisting user orientation |
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%in several different user interfaces; our primary application |
%in several different user interfaces; our primary application |
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%is navigating hyperstructures using Focus+Context views. |
%is navigating hyperstructures using Focus+Context views. |
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% |
% |
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%We can rapidly generate a texture for any document the user visits, |
%We can rapidly generate a texture for any document the user visits, |
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%and the user will be able to learn the textures of the |
%and |
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%most often visited |
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%documents, as per Zipf's law. |
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% |
% |
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%In Focus+Context views, the textures can act as visual cues in the context |
%In Focus+Context views, the textures can act as visual cues in the context |
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%(information foraging). |
%(information foraging). |
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We discuss our freely available hardware-accelerated implementation |
We discuss our freely available hardware-accelerated implementation |
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of unique backgrounds |
of unique backgrounds |
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on the NV10 and NV25 architectures, and |
on the NV10 and NV25 architectures. |
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show an example user interface for browsing linked PDF documents |
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in a focus+context view using unique backgrounds. |
We show the results of an initial experiment ... XXX |
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Text readability is a major concern for using such backgrounds, |
Text readability is a major concern for using such backgrounds, |
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and we discuss a method for enhancing readability by unnoticeably |
and we discuss a method for enhancing readability by both providing |
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bleaching the background around text.. |
fast, interactive zooming and |
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unnoticeably bleaching the background around text. |
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% The implementation works by combining a small set of basis textures |
% The implementation works by combining a small set of basis textures |
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% and perceptually chosen colors |
% and perceptually chosen colors |
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% data identity |
% data identity |
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% to make |
% to make |
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% similar but distinct objects distinguishable and easily recognizable. |
% similar but distinct objects distinguishable and easily recognizable. |
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Discrete, unordered variables often occur coupled to other |
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variables when drawing graphs; for example, a variable representing |
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different makes of cars would be such. |
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If the number of different values that the variable takes are few, |
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distinct symbols can be used... |
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Visualizing a discrete variable with unordered values |
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In this article, we introduce the use of procedurally generated unique backgrounds |
In this article, we introduce the use of procedurally generated unique backgrounds |
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as a visualization of data identity: if each data item with a different identity has |
as a visualization of document identity: if each document has |
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a different, easily distinguishable texture, the user can become aware of the identity |
a different, easily distinguishable background texture, the user can become aware of the identity |
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of a displayed item at a glance, without explicitly reading the title. |
of a displayed item at a glance, without explicitly reading the title. |
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Even more importantly, the user can become aware of the identity just by seeing |
The user can even become aware of the identity just by seeing |
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any {\em fragment} of the item, instead of the ``title page''. |
any {\em fragment} of the item, instead of the title page. This property is vital |
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for our example application discussed in Section~\ref{secbuoyoing}. |
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% as a navigation aid in focus+context views. |
% as a navigation aid in focus+context views. |
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% a more prominent target for tracking movement between views. |
% a more prominent target for tracking movement between views. |
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In the following sections, |
In the following sections, |
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we first review related work on texturing, |
we first review related work on texturing. |
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Focus+Context views. |
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Next, we discuss the motivating example for this work: |
Next, we discuss the motivating example for this work: |
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a specific (xupdf XXX) focus+context |
the BuoyOING focus+context |
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user interface to a hypertext structure. |
user interface to a hypertext structure. |
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Then, we formulate general principles for designing |
Then, we formulate general principles for designing |
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recognizable backgrounds and present a hardware-accelerated implementation. |
recognizable backgrounds and present a hardware-accelerated implementation. |
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Following this, we discuss enhancing text readability on such backgrounds |
Following this, we discuss enhancing text readability on such backgrounds |
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and practical experiences. |
and practical experiences. |
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Finally, we show an example application of unique backgrounds |
%Finally, we show an example application of unique backgrounds |
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for browsing linked PDF documents in a focus+context view. |
%for browsing linked PDF documents in a focus+context view. |
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\section{Related work} |
\section{Related work} |
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\subsection{Texturing} |
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The {\em texture} of a surface, taken literally, is its translation-invariant statistical microstructure. |
The {\em texture} of a surface, taken literally, is its translation-invariant statistical microstructure. |
228 |
In computer graphics, |
In computer graphics, |
229 |
the word {\em texturing} is used in |
the word {\em texturing} is used in |
330 |
%dimensionality. |
%dimensionality. |
331 |
%% XXX: this is something we should experiment with our textures |
%% XXX: this is something we should experiment with our textures |
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\subsection{Focus+Context views} |
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Focus+Context, or, fisheye views\cite{fc-fisheye} are |
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a paradigm for viewing large, |
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structured information sets |
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by showing the current area of |
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interest (focus) magnified |
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and the structurally connected but further-away |
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elements peripherally, with less magnification. |
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Much of the work on focus+context views has |
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concentrated on tree structures\cite{lamping96hyperbolic,fc-images}, |
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or flat 2D images or maps\cite{fc-taxonomy}. |
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333 |
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334 |
% The type of focus+context view for whi |
% The type of focus+context view for whi |
335 |
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423 |
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\section{The motivation for Unique Backgrounds: the BuoyOING user interface} |
\section{The motivation for Unique Backgrounds: the BuoyOING user interface} |
425 |
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426 |
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\label{secbuoyoing} |
427 |
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428 |
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Focus+Context, or, fisheye views\cite{fc-fisheye} are |
429 |
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a paradigm for viewing large, |
430 |
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structured information sets |
431 |
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by showing the current area of |
432 |
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interest (focus) magnified |
433 |
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and the structurally connected but further-away |
434 |
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elements peripherally, with less magnification. |
435 |
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Much of the work on focus+context views has |
436 |
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concentrated on tree structures\cite{lamping96hyperbolic,fc-images}, |
437 |
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or flat 2D images or maps\cite{fc-taxonomy}. |
438 |
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439 |
TJL |
TJL |
440 |
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441 |
The motivating example for |
The motivating example for |
1289 |
\fi |
\fi |
1290 |
\caption{ |
\caption{ |
1291 |
\label{fig-zipf} |
\label{fig-zipf} |
1292 |
Zipf's law concretized: why remembering 15 textures helps. |
Zipf's law concretized: why distinguishing 15 |
1293 |
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textures from a large number of others helps. |
1294 |
In real life, accesses to documents often follow Zipf's law, meaning that |
In real life, accesses to documents often follow Zipf's law, meaning that |
1295 |
some documents get accessed far more often than most. |
some documents get accessed far more often than most. |
1296 |
Each square represents a document, and the area of each square is scaled |
Each square represents a document, and the area of each square is scaled |
1297 |
to its rate of accesses. |
to its rate of accesses. |
1298 |
The diagram shows 2000 documents weighted with Zipf's law with exponent 1.1. |
The diagram shows 2000 documents weighted with Zipf's law with exponent 1.1. |
1299 |
The 15 most important documents account for 50\% of the accesses. |
Here, the 15 most important documents account for approximately half of the accesses. |
1300 |
% 0.50469672124463749 |
% 0.50469672124463749 |
1301 |
} |
} |
1302 |
\end{figure} |
\end{figure} |
1521 |
% |
% |
1522 |
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1523 |
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1524 |
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Discrete, unordered variables often occur coupled to other |
1525 |
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variables when drawing graphs; for example, a variable representing |
1526 |
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different makes of cars would be such. |
1527 |
|
If the number of different values that the variable takes are few, |
1528 |
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distinct symbols can be used... |
1529 |
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1530 |
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Visualizing a discrete variable with unordered values |
1531 |
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1532 |
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1533 |
\section{Acknowledgments} |
\section{Acknowledgments} |
1534 |
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1535 |
The authors would like to thank John Canny, |
The authors would like to thank John Canny, |