A Planning-Based Image Segmentation System
Tajen Liang, STANLEY M. DUNN, Stephen Shemlon, Casimir A. Kulikowski · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1990
Most of the successful image understanding systems work because the domain is sufficiently restricted. Although variables such as the image formation process, objects in the domain and the interpretation tasks are explicitly constrained, there are many implicit constraints in the image processing steps. It can be difficult to generalize from these exemplary image understanding systems since we do not know how general this choice of steps is or if they were chosen since they "work the best." To generalize to other image understanding tasks and domains requires an explicit understanding of why the operators were chosen and their performance. Although a suitable segmentation is required for computing an accurate interpretation, no one set of low level operators will work in all image domains. A more promising approach is to develop a segmentation plan generator which uses the application goal as well as intrinsic and domain-specific knowledge to guide the segmentation processes. This paper demonstrates an intermediate level vision system which can generate an initial segmentation plan and then refines the plan to produce an optimal segmentation (for the desired goal) of the scene. The plan organizes the domain-independent and domain-dependent knowledge in a systematic way that makes the system suitable for a large and varied set of domains. This paper describes the planning approach to segmentation, an outline of the steps involved and results of the first experiments using the plan generator to segment panoramic dental radiographs.