Description
In this project you will design and develop a new visualization technique that aims to enhance traditional scatterplots by integrating flow-like structures using Line Integral Convolutions (LICs). This technique will overlay continuous, smooth curves onto time-varying or parametrized scatterplots, revealing hidden trends, motion patterns, and structural relationships in complex datasets. By blending vector field visualization with point-based data representation, SLICs aim to uncover patterns that might be obscured in static scatterplots.
Tasks
- Compute Line Integral Convolutions (LICs) to generate smooth, flow-like patterns from parametric or time-varying scatterplot data to reveal motion trends and evolving structures.
- Enhance scatterplots with directional cues by embedding lines that follow data-driven vector fields.
- Analyze emerging patterns and structures to uncover hidden relationships in high-dimensional datasets.
- Optimize visualization parameters to balance clarity, density, and interpretability.
- Evaluate the final outcomes.
Requirements
- Familiarity with vector fields and flow visualization.
- Experience with Python or Javascript for data visualization.
- Understanding of line integral convolutions (LICs) is helpful.
Environment
The project should be implemented as a standalone application, desktop or web-based (to be discussed).