Object shape and size recognition from tactile images

Artificial touch sensing system for various Human Computer Interaction (HCI) applications is required to be capable of recognizing various parameters viz. object shape, size, texture and surface. However, only identifying object-shapes is not sufficient for object recognition. It is necessary to distinguish the object shapes according to their dimensions or sizes. Thus in the present work object shapes as well as their sizes are recognized by processing and analysis of tactile images obtained by grasping different objects. In this study, statistical features are extracted from a number of acquired tactile images for classification in their respective object shape and size classes. Both inter-subject and intra-subject classifications are performed using four different classifiers (k-nearest neighbor (kNN), Naïve Bayes classifier, Linear Discriminant Analysis (LDA) and Ensemble) in one-versus-one (OVO) basis, which resulted in high classification accuracy independent of the type of classifier. The mean classification accuracies for inter-subject and intra-subject shape and size recognition are found to be 93%, 87% and 94% and 88% respectively.