reorder some slides

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2025-11-23 15:29:42 +01:00
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commit 74ae33c267

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@@ -61,38 +61,23 @@
% SECTION 1: INTRODUCTION
%----------------------------------------------------------------------------------------
\section{Introduction}
\begin{frame}{Goal of the Paper}
\begin{itemize}
\item Scatterplots are fundamental for exploring multidimensional data
\item But: with large datasets they suffer from \textbf{overplotting}
\item Dense regions obscure structure, samples become inaccessible
\item Goal:
\begin{itemize}
\item Reduce clutter
\item Preserve neighborhood relations
\item Achieve uniform sample distribution
\item Maintain interpretability
\end{itemize}
\end{itemize}
\end{frame}
%----------------------------------------------------------------------------------------
% SECTION 2: PROBLEM
%----------------------------------------------------------------------------------------
\section{Problem: Overplotting}
\section{Introduction}
\begin{frame}{Why Scatterplots Clutter}
\begin{frame}{Problem: Scatterplots Clutter}
\begin{itemize}
\item Scatterplots are fundamental for exploring multidimensional data
\item Modern datasets: millions of samples
\item Pixel resolution fixed → many samples map to the same pixel
\item This results in \textbf{overplotting}
\item Consequences:
\begin{itemize}
\item Occlusion of clusters + outliers
\item Occlusion of clusters
\item Loss of density information
\item Hard to select individual items
\item Hard to select and see individual items
%\item Misleading visual perception
\end{itemize}
\item A method is needed to \textbf{declutter} without losing structure
@@ -105,6 +90,18 @@
\footnotesize\text{Source: \cite{statisticsglobe_overplotting_r}}
\end{frame}
\begin{frame}{Goal of the Paper}
\begin{itemize}
\item Goal:
\begin{itemize}
\item Reduce clutter
\item Preserve neighborhood relations
\item Achieve uniform sample distribution
\item Maintain interpretability
\end{itemize}
\end{itemize}
\end{frame}
\begin{frame}{Limitations of Traditional Approaches}
\begin{itemize}
\item Transparency-based methods
@@ -124,12 +121,11 @@
\item Need a \textbf{global}, \textbf{smooth}, \textbf{monotonic}, \textbf{collision-free} method
\end{itemize}
\end{frame}
%----------------------------------------------------------------------------------------
% SECTION 3: BACKGROUND
%----------------------------------------------------------------------------------------
\section{Background: Density Fields \& Integral Images}
\section{Background:\\Density Fields \& Integral Images}
\begin{frame}{Density Estimation}
\begin{itemize}
@@ -400,21 +396,6 @@
\includegraphics[scale=0.4]{rsc/img}
\end{frame}
% --- THE END
\begin{frame}[focus]
Thanks for your Attention!
\end{frame}
%----------------------------------------------------------------------------------------
% CLOSING/SUPPLEMENTARY SLIDES
%----------------------------------------------------------------------------------------
\appendix
\section{Backup}\label{sec:backup}
\begin{frame}{Example I}
\centering
\includegraphics[scale=0.1]{rsc/2408.06513v1_page_8_1}\\
@@ -451,6 +432,40 @@
\footnotesize\text{Source: \cite{Rave_2025}}
\end{frame}
% --- THE END
\begin{frame}[focus]
Thanks for your Attention!
\end{frame}
%----------------------------------------------------------------------------------------
% CLOSING/SUPPLEMENTARY SLIDES
%----------------------------------------------------------------------------------------
\appendix
\section{Backup}\label{sec:backup}
\begin{frame}{Domain Transformation (Molchanov \& Linsen)}
\begin{itemize}
\item Integral Images $\rightarrow$ Transformation mapping
\item Definition:
\[
t(x,y; d) = \frac{
\alpha q_1 + \beta q_2 + \gamma q_3 + \delta q_4
+ \alpha_t (x,1) + \beta_t (1,y) + \gamma_t (x,0) + \delta_t (0,y)
}{2C}
\]
\item Problems:
\begin{itemize}
\item Not identity for uniform density
\item Iteration unstable
\item Does not converge to equalized distribution
\end{itemize}
\end{itemize}
\end{frame}
\begin{frame}{Sources}
\nocite{*} % Display all references regardless of if they were cited
\bibliography{sources}