The authors present the design and implementation of FormPlus, a form authoring toolkit for on-screen form design and filing. It is implemented on an IBM OS/2 Presentation Manager platform. FormPlus provides major front-end functions for form processing such as converting paper forms into electronic forms, designing forms, and filling in forms. No knowledge about programming is necessary. FormPlus also provides an object-oriented API (application programming interface) for programmers to develop their own applications with minimum effort. FormPlus is designed to be easily extendable to incorporate new objects and to allow data import and export.>

The authors provide a complete method for describing and recognizing 3-D objects, using surface information. Their system takes as input dense range date and automatically produces a symbolic description of the objects in the scene in terms of their visible surface patches. This segmented representation may be viewed as a graph whose nodes capture information about the individual surface patches and whose links represent the relationships between them, such as occlusion and connectivity. On the basis of these relations, a graph for a given scene is decomposed into subgraphs corresponding to different objects. A model is represented by a set of such descriptions from multiple viewing angles, typically four to six. Models can therefore be acquired and represented automatically. Matching between the objects in a scene and the models is performed by three modules: the screener, in which the most likely candidate views for each object are found; the graph matcher, which compares the potential matching graphs and computes the 3-D transformation between them; and the analyzer, which takes a critical look at the results and proposes to split and merge object graphs.>

A method is developed to extract important curves, corresponding to physical boundaries of objects, from a range image. It is shown how to infer, from these curves, a segmentation of the scene into surface patches, and how to use these descriptions to establish correspondences between two scenes. In a first step, labeled curves corresponding to jump boundaries, creases, and limbs of objects are grouped into boundaries of regions. Each region is therefore described by its boundaries and by a polynomial approximation, which allows each path individually and also the complete scene to be reconstructed. In a second step, objects (or partial objects) are inferred from surface patches, and then two range images are at this partial object level. Graphs of objects are matched using a best-first search under three types of constraints: unary constraints between corresponding nodes, binary constraints between corresponding linked pairs of nodes, and constraints imposed by the computed geometric transformation. Substantial partial occlusion is allowed. The generality and robustness of this approach is illustrated by several examples.>

A method to segment and describe visible surfaces of three-dimensional (3-D) objects is presented by first segmenting the surfaces into simple surface patches and then using these patches and their boundaries to describe the 3-D surfaces. First, distinguished points are extracted which will comprise the edges of segmented surface patches, using the zero-crossings and extrema of curvature along a given direction. Two different methods are used: if the sensor provides relatively noise-free range images, the principal curvatures are computed at only one resolution, otherwise, a multiple scale approach is used and curvature is computed in four directions 45° apart to facilitate interscale tracking. These points are then grouped into curves and these curves are classified into different classes which correspond to significant physical properties such as jump boundaries, folds, and ridge lines (or smooth extrema). Then jump boundaries and folds are used to segment the surfaces into surface patches, and a simple surface is fitted to each patch to reconstruct the original objects. These descriptions not only make explicit most of the salient properties present in the original input, but are more suited to further processing, such as matching with a given model. The generality and robustness of this approach is illustrated on scene images with different available range sensors.