Abstract
Even if some of previous approaches prove their effectiveness for tightly controlled environments such as industrial settings, dependable object recognition remains difficult in real environments. Thus, this paper proposes a method of robust object recognition effective in real environments. The basic idea is to recognize and predict objects via a combined use of ontology and Bayesian network. To demonstrate the benefits of the proposed approach, a case study is conducted in an actual working environment.
