Generation of views of TV content using TV viewers' perspectives expressed in live chats on the web
Proceedings of the 13th annual ACM international conference on Multimedia
Virtualized Reality: Perspectives on 4D Digitization of Dynamic Events
IEEE Computer Graphics and Applications
Tracking and recognizing actions of multiple hockey players using the boosted particle filter
Image and Vision Computing
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
3D Video and Its Applications
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Humans see things from various viewpoints but nobody attempts to see anything from every viewpoint owing to physical restrictions and the great effort required. Intelligent interfaces for viewing multi-viewpoint videos may remove the restrictions in effective ways and direct us toward a new visual world. We propose an agent-assisted multi-viewpoint video viewer that incorporates (1) target-centered viewpoint switching and (2) social viewpoint recommendation. The viewer stabilizes an object at the center of the display field using the former function, which helps to fix the user's gaze on the target object. To identify the popular viewing behavior for particular content, the latter function exploits a histogram of the viewing log in terms of time, viewpoints, and the target of many personal viewing experiences. We call this knowledge source of the director agent a viewgram. The agent automatically constructs the preferred viewpoint sequence for each target. We conducted user studies to analyze user behavior, especially eye movement, while using the viewer. The results of statistical analyses showed that the viewpoint sequence extracted from a viewgram includes a more distinct perspective for each target, and the target-centered viewpoint switching encourages the user to gaze at the display center where the target is located during the viewing. The proposed viewer can provide more effective perspectives for the main attractions in scenes.