Enhancing-Image-Segmentation-with-Eye-tracking

This repository contains code and data used for the final project in the class "Computational Cognitive Science III" at the University of Copenhagen.

Analysis-of-Fixation-Patterns

• Determining-the-dominant-eye.ipynb

  Code to determine the dominant eye of a viewer.

• analysis-of-fixation-patterns.ipynb

  Code to analyse which fixation point (longest duration, first, last, etc.) usually lies on the object.

• one-fixation-point-for-sam.ipynb

  Code to explore whether the fixation point with the longest duration yields better segmentation masks.

Datasets

This folder contains the eye tracking data (fixation points) extracted from the POET dataset.

• eye_tracking_data.csv

• eye_tracking_data.pkl.zip

Data-Preprocessing

• exploring-eye-tracking-data-POET.ipynb

  Code to analyse the eye tracking data of the POET data set and store it as a .csv and .pkl file for further processing.

SAM

• compare-segmentations-with-ground_truth.ipynb

  Code to calculate the Dice coefficient between segmentation masks generated by passing SAM human fixation points and points generated by Ablation or GradCAM. Addiotnally, performs Wilcoxon signed-rank tests to see whether differences in Dice coefficients between human and model generated ppoints are significant.