grouping

This paper presents a novel image editing program emphasizing easy selection and manipulation of material found in informal, casual documents such as sketches, handwritten notes, whiteboard images, screen snapshots, and scanned documents. The program, called ScanScribe, offers four significant advances. First, it presents a new, intuitive model for maintaining image objects and groups, along with underlying logic for updating these in the course of an editing session. Second, ScanScribe takes advantage of newly developed image processing algorithms to separate foreground markings from a white or light background, and thus can automatically render the background transparent so that image material can be rearranged without occlusion by background pixels. Third, ScanScribe introduces new interface techniques for selecting image objects with a pointing device without resorting to a palette of tool modes. Fourth, ScanScribe presents a platform for exploiting image analysis and recognition methods to make perceptually significant structure readily available to the user. As a research prototype, ScanScribe has proven useful in the work of members of our laboratory, and has been released on a limited basis for user testing and evaluation.

The human visual system makes a great deal more of images than the elemental marks on a surface. In the course of viewing, creating, or editing a picture, we actively construct a host of visual structures and relationships as components of sensible interpretations. This paper shows how some of these computational processes can be incorporated into perceptually-supported image editing tools, enabling machines to better engage users at the level of their own percepts. We focus on the domain of freehand sketch editors, such as an electronic whiteboard application for a pen-based computer. By using computer vision techniques to perform covert recognition of visual structure as it emerges during the course of a drawing/editing session, a perceptually supported image editor gives users access to visual objects as they are perceived by the human visual system. We present a flexible image interpretation architecture based on token grouping in a multiscale blackboard data structure. This organization supports multiple perceptual interpretations of line drawing data, domain-specific knowledge bases for interpretable visual structures, and gesture-based selection of visual objects. A system implementing these ideas, called PerSketch, begins to explore a new space of WYPIWYG (What You Perceive Is What You Get) image editing tools.

The human visual system makes a great deal more of images than the elemental marks on a surface. In the course of viewing, creating, or editing a picture, we actively construct a host of visual structures and relationships as components of sensible interpretations. This paper shows how some of these computational processes can be incorporated into perceptually-supported image editing tools, enabling machines to better engage users at the level of their own percepts. We focus on the domain of freehand sketch editors, such as an electronic whiteboard application for a pen-based computer. By using computer vision techniques to perform covert recognition of visual structure as it emerges during the course of a drawing/editing session, a perceptually supported image editor gives users access to visual objects as they are perceived by the human visual system. We present a flexible image interpretation architecture based on token grouping in a multiscale blackboard data structure. This organization supports multiple perceptual interpretations of line drawing data, domain-specific knowledge bases for interpretable visual structures, and gesture-based selection of visual objects. A system implementing these ideas, called PerSketch, begins to explore a new space of WYPIWYG (What You Perceive Is What You Get) image editing tools.