A 2D Flow Visualization User Study Based on Eye Tracking Analysis

• Main Authors: Ho, Hsia-Yang, 何昕暘
• Other Authors: Weng, Chih-Wen
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/44435126136066072266

碩士 === 國立交通大學 === 應用數學系所 === 100 === The flow visualization methods are widely applied in many areas. A good flow visualization method would help people understand the flow quickly and accu-rately, so how to present the structure and featurs of a flow field in a much clearer and more understandable way is a very important issue for evaluating a flow vi-sualiztion method. Previous user studies on flow visualization methods compared the effectiveness of different methods by analyzing the users’ response to ques-tions. In this thesis, we propose an eye-movement-based user study methodology to analyze and evaluate flow visualization methods. We observe some phenom-ena that were not found in the previous questionnaire-based evaluation studies on flow visualization methods. Our experimential results help us understand human subjects’ visual behavior when they view the flow images, and the presentation efficiency of different flow visualization methods. We compared five 2D flow visu-alization methods in our experiments, including a direct flow visualization method, two geometric flow visualization methods and two texture-based flow visualization methods. According to fluid dynamics experts’ suggestions and previous flow visu-alization evaluation studies, display of critical points and flow trajectory prediction are two important functions of flow visualization. In order to evaluate these two aspects, we designed the following four tasks: (1) Free-viewing: we analyze the correlation of fixations with flow velocity, and the fixations with location of crit-ical points while the user is not performing any tasks. We also develop a model to predict the location of the eye gaze under free-viewing. (2) Advection predict-ing: compare subject’s advection predicting performance on different visualization methods. (3) Flow features locating: compare subject’s flow feature finding perfor-mance on different visualization methods. (4) Flow features identifying: compare subject’s flow feature identification performance on different visualization meth-ods. Besides the eye-gaze data, we also compared the result of response time and the location that mouse clicked. Our experiments show that human subjects tend to be attracted by the region where the flow field has large directional variations on all five flow visualization methods. On the other hand, experts attend to the criti-cal points more than non-experts, and the response time of experts is shorter than non-experts, while there are no significant differences in accuracy and correctness between experts and non-experts. Among all the methods used in our experiment, texture-based methods perform well at the correctness and the response time, but their presentation of flow features cannot draw subjects’ attention well.