- Publication Details
- In Proc. CHI 2010
- Apr April 10, 2010
ReBoard is a system for capturing and retrieving whiteboard information that leverages the affordances of conventional office whiteboards while mitigating their shortcomings.
The use of whiteboards is pervasive across a wide range of work domains. Whiteboards enable users to quickly externalize an idea or concept, to facilitate understanding among collaborators and peers, and can serve as a conversational artifact to ground subsequent discussion. Several studies of workplace practice have shown the value of these benefits. Whiteboards have been shown to support a multitude of collaborative activities, including awareness , design [2,3], publishing , and medicine .
A wide range of commercial and research tools have been developed to marry the affordances of digital tools and usability of whiteboards. For example, Collaborage  enables users to link whiteboard content to the web. Despite the availability of these tools, their use is all but nonexistent in the modern workplace while traditional whiteboards are ubiquitous. We attribute the lack of adoption to the fact that while existing tools provide a bridge from physical to digital representations, they do not provide effective methods for interaction and use of information once it is digitized. Furthermore, the affordances of electronic whiteboards for writing are worse than those of conventional boards, casting doubt on their value proposition.
How can we leverage affordances of conventional office whiteboards while mitigating their shortcomings?
- Automated capture
- Image processing to compensate for distortion
- Archival and retrieval by useful metadata
- Interfaces for browsing and sharing
We have built and evaluated ReBoard, a system for capturing and retrieving information from whiteboards. Capture occurs through a mixed-initiative process that combines manual capture with automatic detection of significant changes to board content. Images are corrected for distortion due to off-axis camera location and contrast is adjusted for a more legible image. Images are indexed by whatever metadata are available: the location of the board, the time of capture, the location of the changed region, who was present, whether the image was shared, etc. We then allow the user to browse whiteboard content through a web application that retrieves images based on whatever episodic memory of the desired images (location, time, camera, sharing, etc.) the user had. We have published some details of the ReBoard system here.
We have also evaluated the ReBoard system through a longitudinal deployment. We found that people used the system to establish new work flows around sharing and reusing content that were not possible without ReBoard. This work underscored the differences between previous capture systems and ReBoard: other systems stopped at the capture phase, allowing users only to save the contents to a file. ReBoard, on the other hand, allowed people to make use of the captured images in their work, thereby transforming a range of communication and collaboration practices. This work has been published in our CHI 2010 paper.
We have also experimented with the incorporation of content-based tagging of whiteboard images. We see content tags as physical embodiments of associated digital media. We extended ReBoard to match high-resolution snapshots taken with a hand-held digital camera or cell phone to overview images captured automatically by ReBoard cameras. This automated linking process makes it possible to situate high-quality images within the overall image, and to record specific annotations on these detailed images. More information can be found in our IEEE Pervasive Computing article.
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Related blog posts.
- Publication Details
- IEEE Pervasive Computing. 9(2). 46-55.
- Mar March 15, 2010