visual

Asynchronous collaborators often use freeform ink annotations to point to visually salient perceptual features of line charts such as peaks or humps, valleys, rising slopes and declining slopes. We present a set of techniques for interpreting such annotations to algorithmically identify the corresponding perceptual parts. Our approach is to first apply a parts-based segmentation algorithm that identifies the visually salient perceptual parts in the chart. Our system then analyzes the freeform annotations to infer the corresponding peaks, valleys or sloping segments. Once the system has identified the perceptual parts it can highlight them to draw further attention and reduce ambiguity of interpretation in asynchronous collaborative discussions.

Computer users with motor impairments face major challenges with conventional mouse pointing. These challenges are mostly due to fine pointing corrections at the final stages of target acquisition. To reduce the need for correction-phase pointing and to lessen the effects of small target size on acquisition difficulty, we introduce four enhanced area cursors, two of which rely on magnification and two of which use goal crossing. In a study with motor-impaired and able-bodied users, we compared the new designs to the point and Bubble cursors, the latter of which had not been evaluated for users with motor impairments. Two enhanced area cursors, the Visual-Motor-Magnifier and Click-and-Cross, were the most successful new designs for users with motor impairments, reducing selection time for small targets by 19%, corrective submovements by 45%, and error rate by up to 82% compared to the point cursor. Although the Bubble cursor also improved performance, participants with motor impairments unanimously preferred the enhanced area cursors.