recognition of freehand sketches using mean shift
Freehand sketching is a natural and powerful means of interpersonal communication. But to date, it still cannot be supported effectively by human-computer interface. In this paper, we propose a robust method for sketch recognition. It uses mean shift, a nonparametric technique which can delineate arbitrarily shaped clusters, as a pre-process to analyze the direction-curvature joint space and suppress the severe noise of sketched strokes. Furthermore, it combines the vertex detection and primitive shape approximation into a unified and incremental procedure which, by fully utilizing the visual features, can handle hybrid and smooth curves gracefully. Our method does not rely on any domain-specific knowledge, and therefore it can be easily integrated with other high-level applications
recognition sketching mean shift mean interpersonal communication human-computer interfaces paper robust method sketch recognition shape cluster space noise stroke detection approximation incremental visualization feature hybrid smooth knowledge integrability applications hci direct multimodal interface process