map

Ramonamap is an iterative map for database and communication services within our workgroup. Resources are represented as icons on the map, which preserves their actual (or implied) physical location and capitalizes on a user's understanding of maps. The map is interactive, giving the user control over the level of detail visible, allowing more information and services to appear than could be placed on a static map. The interactivity also allows users to change the map and add icon annotations. Since the map is continuously derived from an on-line database, changes and annotations are immediately shared by all users. As the database contains a wealth of information about the group, it also serves as a source for static maps for other purposes.

Navigation services (e.g., in-car navigation systems and online mapping sites) compute routes between two locations to help users navigate. However, these routes may direct users along an unfamiliar path when a familiar path exists, or, conversely, may include redundant information that the user already knows. These overly complicated directions increase the cognitive load of the user, which may lead to a dangerous driving environment. Since the level of detail is user specific and depends on their familiarity with a region, routes need to be personalized. We have developed a system, called MyRoute, that reduces route complexity by creating user specific routes based on a priori knowledge of familiar routes and landmarks. MyRoute works by compressing well known steps into a single contextualized step and rerouting users along familiar routes.

Image retargeting is the problem of adapting images for display on devices different than originally intended. This paper presents a method for adapting large images, such as those taken with a digital camera, for a small display, such as a cellular telephone. The method uses a non-linear fisheye-view warp that emphasizes parts of an image while shrinking others. Like previous methods, fisheye-view warping uses image information, such as low-level salience and high-level object recognition to find important regions of the source image. However, unlike prior approaches, a non-linear image warping function emphasizes the important aspects of the image while retaining the surrounding context. The method has advantages in preserving information content, alerting the viewer to missing information and providing robustness.

Thumbnail images provide users of image retrieval and browsing systems with a method for quickly scanning large numbers of images. Recognizing the objects in an image is important in many retrieval tasks, but thumbnails generated by shrinking the original image often render objects illegible. We study the ability of computer vision systems to detect key components of images so that automated cropping, prior to shrinking, can render objects more recognizable. We evaluate automatic cropping techniques 1) based on a general method that detects salient portions of images, and 2) based on automatic face detection. Our user study shows that these methods result in small thumbnails that are substantially more recognizable and easier to find in the context of visual search.