A fuzzy rule based effective feature selection approach for augmented reality

Article type: Research Article

Authors: Rajendra Thilahar, C.^{; *} | Sivaramakrishnan, R.^{; *}

Affiliations: Department of Production Technology, MIT, Anna University, Chennai, India

Correspondence: [*] Corresponding authors. C. Rajendra Thilahar, E-mail: [email protected]. and R. Sivaramakrishnan, E-mail: [email protected].

Abstract: A tele-operated robot stereo vision system is used for stretching out the operator’s eye-hand motion and its distance based co-ordination with experts. The major challenge is the reduction of communication delay by using effective decisions to avoid tele-operation instability. This problem can be handled effectively by using the principles of Augmented Reality which provides facilities for superimposing virtual objects onto the real video images of the workspace to create a simulation plan in the client system. In this paper, we propose a new feature selection algorithm called Fuzzy Rules and Information Gain Ratio based Feature Selection Algorithm for selecting the optimal number of features from the full set of available features. Also, a new Fuzzy Rule based Neuro-Genetic Classification Algorithm is proposed in this paper for classifying the augmented images more accurately. The main advantages of the proposed model are reduction in classification and communication time and increase in decision accuracy.

Keywords: Image classification, augmented reality, virtual reality, fuzzy systems, robot motion

DOI: 10.3233/JIFS-191674

Journal: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 5045-5054, 2020