Researchers analyze images in areas such as geology, bat cardiovascular systems and art studies to verify their observations. Some images are hard to study as their details are not vivid; hence there is a need to enhance these images to facilitate their study while preserving their contents. This study is aimed at assisting the researchers in the Cardiovascular Systems Dynamic Laboratory at Texas A&M University by evaluating the importance of Image Enhancement (IE) for improving visibility of features. For this study the images were collected and manipulated using various IE techniques and were shown to the novice researchers who were asked to perform three different tasks. These tasks were representative of the research work conducted in the lab. The techniques that were selected aimed at reducing the problems that are usually associated with data obtained from microscopic feeds. A customized application was developed to expedite and automate the study. The results indicated that the researchers did not immmensely benefit from the improved visualization for easy tasks. However, their performance improved for tasks that required more practice and skill. Our approach contributes towards designing an effective training program for novice researchers in the lab. Moreover, it is promising for similar research in different fields of study.

Providing computer-aided support for human analysis of videos has been a battle of extremes. Powerful solutions exist, but they tend to be domain-specific and complex. The user-friendly, simple systems provide little analysis support beyond basic media player functionality. We propose a human-centered, domain-independent solution between these two points. Our proposed model and system, VAST, is based on our experience in two diverse video analysis domains: science and athletics. Multiple-perspective location metadata is used to group related video clips together. Users interact with these clip groups through a novel interaction paradigm ? views. Each view provides a different context by which users can judge and evaluate the events that are captured by the video. Easy conversion between views allows the user to quickly switch between contexts. The model is designed to support a variety of user goals and expertise with minimal producer overhead. To evaluate our model, we developed a system prototype and conducted several rounds of user testing requiring the analysis of volleyball practice videos. The user tasks included: foreground analysis, ambiguous identification, background analysis, and planning. Both domain novices and experts participated in the study. User feedback, participant performance, and system logs were used to evaluate the system. VAST successfully supported a variety of problem solving strategies employed by participants during the course of the study. Participants had no difficulty handling multiple views (and resulting multiple video clips) simultaneously opened in the workspace. The capability to view multiple related clips at one time was highly regarded. In all tasks, except the open-ended portion of the background analysis, participants performed well. However, performance was not significantly influenced by domain expertise. Participants had a favorable opinion of the system?s intuitiveness, ease of use, enjoyability, and aesthetics. The majority of participants stated a desire to use VAST outside of the study, given the opportunity.