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Scanpath visualization and comparison using visual aggregation techniques.

Vsevolod Peysakhovich¹, Christophe Hurter²

¹ISAE-SUPAERO , France.

Journal of Eye Movement Research

|April 8, 2021

Summary

This summary is machine-generated.

This study introduces visual aggregation methods for clear scanpath visualization. These techniques enhance understanding of eye movement patterns in tasks like visual search and art perception.

Keywords:

edge bundling eye tracking fixation clustering flow directional map mean-shift oriented line integral convolution saccades scanpath visualization

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Last Updated: Nov 9, 2025

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Area of Science:

Human-Computer Interaction
Cognitive Science
Data Visualization

Background:

Scanpath analysis is crucial for understanding visual attention and information processing.
Existing methods for visualizing scanpaths often result in cluttered representations, hindering interpretation.
Developing effective visualization techniques is essential for gaining insights into eye movement data.

Purpose of the Study:

To demonstrate novel visual aggregation techniques for creating non-cluttered scanpath representations.
To enable detailed analysis of eye movement data, including fixation points and saccades.
To provide a framework for comparing scanpaths using similarity metrics.

Main Methods:

Clustering fixation points using the mean-shift algorithm.
Aggregating saccades with the Attribute-Driven Edge Bundling (ADEB) algorithm, considering saccade attributes.
Visualizing flow direction maps using Oriented Line Integral Convolution (OLIC) and computing similarity maps via cosine similarity.

Main Results:

Achieved non-cluttered visual representations of scanpaths.
Successfully aggregated saccades based on multiple attributes.
Developed a method for comparing scanpaths through flow direction map similarity.
Demonstrated applications in basic patterns, visual search, and art perception tasks.

Conclusions:

The presented visual aggregation techniques offer valuable insights into scanpath exploration.
These methods provide informative illustrations of eye movement data.
The approach facilitates a deeper understanding of visual attention and information processing during tasks.