Scene analysis under closed world assumption

The success of model based object recognition in a closed world depends on the correct choice of parametric primitive shapes or correct choice of a set of basic features. The later could be the result of machine learning. In this paper a choice of parametric primitive shapes is made and a recognition procedure including a set of developed algorithms is presented. The procedure is in essence a divide and conquer paradigm. The scene is divided into a two sets: (a) set of flat surfaces, and (b) set of objects. The point clouds corresponding to individual objects are extracted. Each extracted cluster is further analyzed into: (a) candidate parametric object, (b) undetermined object parts such as handles, and (c) noise due to sensory and registration errors that represent high frequency signal to be filtered out. This procedure is in the same time an autonomous learning paradigm that enables to memorize the recognized objects and uses this information to answer future questions about the scene. The experimental part is introduced to verify the proposed procedure.