1. A Viable Implementation of a Comparison Algorithm for Regions of Interest John P. Heminghous Computer Science Clemson University jheming@acm.org

2. Motivation  Lack of directly quantitative eye tracking metrics (performance measures commonly used)  Previous work already addresses the problem  Ability to create a nearly automatic analysis tool

3. Previous work  Privitera and Stark 2000 – String Comparison Divided scene into regions using k-means Divided scene into regions using k-means Generated spatial index S p and sequential index S s Generated spatial index S p and sequential index S s Presented four measures Presented four measures Repetitive – same viewer same sceneRepetitive – same viewer same scene Local – different viewers same sceneLocal – different viewers same scene Idiosyncratic – same viewer different scenesIdiosyncratic – same viewer different scenes Global – different viewers different scenesGlobal – different viewers different scenes

4. Previous Work (cont.) Problem – with k-means clustering user needs to predefine how many regions of interest (ROI) should appear in a scene Problem – with k-means clustering user needs to predefine how many regions of interest (ROI) should appear in a scene Defeats the goal of being as automatic as possible Defeats the goal of being as automatic as possible

5. Previous Work (cont.)  Santella and DeCarlo 2004 – Robust Clustering Technique developed based on three key principles Technique developed based on three key principles ConsistencyConsistency No foreknowledgeNo foreknowledge Robustness in the sense that isolated outliers do not affect clustersRobustness in the sense that isolated outliers do not affect clusters Uses a mean shift procedure to converge clusters together Uses a mean shift procedure to converge clusters together

6. Methodology  Validate the implemented algorithm  Subjects instructed to view an intuitive scene should produce high repetitive and local measures  Subjects viewing a complex scene with no instructions should display considerably lower measures

7. Apparatus  Tobii 1750  Sever – AMD 64 Windows XP  Client – AMD Opteron Fedora Core Linux  Data Display & Collection – C++, OpenGL  Data Analysis – C, OpenGL

8. Experimental Design  Six Subjects All male All male Ages 21-42 Ages 21-42  Stimuli Three black screens with randomly placed digits 1-4 Three black screens with randomly placed digits 1-4 First screen repeated after third First screen repeated after third Computer generated (CG) image Computer generated (CG) image

11. Procedure  Subjects were calibrated before and after testing in order to account for slippage  Subjects were instructed to look at each digit  Subjects were informed that each digit would appear for 500ms  Subjects were given no task for the CG image

12. Operation  Raw eye-gaze data (x, y, t) was recorded  Using simple velocity calculations saccades were filtered out (v > 130°/sec)

13. Operation (cont.)  Clustering was performed by mean shifting data until convergence Starting with a set of n points Starting with a set of n points Each point x j is moved to a new locality s(x j ) which is the weighted mean of nearby data points Each point x j is moved to a new locality s(x j ) which is the weighted mean of nearby data points

14. Operation (cont.) Where k is the kernel function that defines the effect the data points have on each other Where k is the kernel function that defines the effect the data points have on each other σ s defines the spatial extent of the kernel σ s defines the spatial extent of the kernel No clusters exist closer in locality than σ s No clusters exist closer in locality than σ s Original data was classified into clusters based on converged copies Original data was classified into clusters based on converged copies

15. Mean Shift Fixation Data Outlier

16. Operation (cont.)  String based comparison was performed Each cluster was assigned a character (A-Z, 0-9, a-z) Each cluster was assigned a character (A-Z, 0-9, a-z) Viewers gaze data throughout clusters generated a string Viewers gaze data throughout clusters generated a string Considering string a and b Considering string a and b S p was computed by dividing the number of characters in both strings by the number of characters in aS p was computed by dividing the number of characters in both strings by the number of characters in a S s was computed by dividing the number of characters in both strings by the Levenshtein distance between a and b and subtracting from oneS s was computed by dividing the number of characters in both strings by the Levenshtein distance between a and b and subtracting from one The Levenshtein distance between two strings strings is based on the cost of three operations: insertion, deletion, and substitution used two transform the second string (b) into the first (a)The Levenshtein distance between two strings strings is based on the cost of three operations: insertion, deletion, and substitution used two transform the second string (b) into the first (a) Only repetitive and local measures calculated Only repetitive and local measures calculated

17. Visualization

18. Results  Based on human’s parafoveal range (5°), σ s = 100 SpSp SsSs numbers11.00 numbers21.000.88 numbers31.000.96 numbers1 (second run)1.000.80 raytrace0.830.43 Local results SpSp SsSs Subject11.000.75 Subject21.000.50 Subject31.000.50 Subject41.000.75 Subject51.000.25 Subject61.000.50 Repetitive results (numbers1)

19. Results (cont.)  σ s = 70 and σ s = 40 SpSp SsSs numbers11.00 numbers21.000.68 numbers30900.83 numbers1 (second run)1.000.72 raytrace0.820.32 Local results SpSp SsSs numbers11.00 numbers20.900.57 numbers30.900.77 numbers1 (second run)0.870.57 raytrace0.530.24 Local results

20. Discussion  Results verified hypothesis  Unchanged pattern with lower σ s values  Repetitive results did not reveal much because too little test data

21. Future Work  Port from C to C++  Design a easy to use GUI interface  Add idiosyncratic measures

22. Questions?