| dc.description.abstract | The visual is one of the important perceptions that human assimilates information from their surroundings. Through analyzing gaze information is a way to explore human visual behaviors and cognitive behaviors. In order to explicitly obtain the content of interested to the audience, the various of gaze mapping functions need to be proposed in accordance with the characteristics of different materials with the diversification of digital information. Visualization is an effective method to concrete the abstract visual behavior. It can help researchers better understand the characteristics or relationships between gaze information and the attended content. Therefore, it could be an attractive issue to study visualization with gaze analysis information in different stimulus conditions for understanding the cognitive behaviors.
These stimulus conditions are classified into three categories, including static, instant static and dynamic stimulus in this dissertation. For the static stimulus, the general visualization patterns, such as areas of interest, hot spots, or foci trajectory, can only be displayed based on the gaze density information. For not only limited to visualizing the statistical data, this dissertation proposes a method for extracting the content of meaningful objects under the instant static stimulus condition. The advantage of this method is the capability to obtain the content of focused object in the web-based, user-controlled, stimulus environment. In terms of dynamic stimulus condition, a new visualization pattern is proposed, called as Note Video, using numerous mini episodes to represent the visual behaviors. The content of these episodes is associated to the focused object. To achieve these mini episodes, the gaze-based automatic focused object tracking (AFOT) method is proposed to clearly and accurately present the visual behaviors while the audience watching the video.
In addition to the objective factors, these stimuli, that influence visual behaviors, the subjective factors, such as gender or interest, could also be examined. For reducing the human intervention during the data collection, the gender classification based on the facial component is proposed to detect the gender of the audience. Consequently, this dissertation discusses factors that influence visual behaviors, not merely the various stimuli but also the gender as well. | en_US |