An assumption-based scene interpretation system that solves multiplicity of scene description

A Scene interpretation system that solves the multiplicity problem of scene description is described. To recognize a structured object, the system is required to segment lines or arcs from fragmented image features, and group them into structured parts of the object. The problem is that, in machine vision, the segmentation and grouping processes cannot determine the correct parts of the object uniquely for themselves. To carry out the interpretation against the problem, the authors have prototyped an assumption-based scene interpretation system (ASIS) that has an inference framework to get a set of consistent hypotheses that imply the goal and observed facts. The hypothetical reasoning scheme of ASIS is realized by a rule base with an assumption-based truth maintenance system (ATMS). By incorporating them, ASIS can obtain the globally plausible and consistent interpretation by preserving the alternative hypotheses in interpreting typical indoor scenes