SURFACES IN EARLY RANGE IMAGE UNDERSTANDING (SEGMENTATION, OBJECT RECOGNITION, DIFFERENTIAL GEOMETRY, DATA-DRIVEN PROCESS, PROCESSING)

Perception of surfaces plays a fundamental role in three-dimensional object recognition and image understanding. A range image explicitly represents the surfaces of objects in a given field of view as an array of depth values. Previous research in range image understanding has limited itself to extensions of edge-based intensity image analysis or to interpretations in terms of polyhedra, generalized cylinders, quadric primitives, or convex objects. Computer vision research has demonstrated the advantages of data-driven early processing of image data. If early processing algorithms are not committed to interpretation in terms of restrictive, domain-specific, high-level models, the same algorithms may be incorporated in different applications without substantial effort.; A general approach has been developed for processing range images to obtain a high-quality, rich (information-preserving), accurate, intermediate-level description consisting of graph surface primitives, the associated segmented regions, and their bounding edges. Only general knowledge about surfaces is used to compute a complete image segmentation; no object level information is involved. The early range image understanding algorithm consists primarily of a differential-geometric, visible-invariant pixel labeling method based on the sign of mean and Gaussian curvatures and an iterative region-growing method based on variable-order surface-fitting of the original image data. The high-level control logic of the current implementation is sequential, but all low-level image processes can be executed on parallel architectures. This surface-based image analysis approach has successfully segmented a wide variety of real and synthetic range images and is also shown to have significant potential for intensity image analysis. It is interesting to note that the surface and edge description algorithms use the same basic "sign-of-curvature" paradigm in different dimensions.