Object-shape classification and reconstruction from tactile images using image gradient

A human explores the world around him through his sense to touch. Touch sensation enables us to understand shape, texture and hardness of an object/surface necessary for efficient exploration. Incorporating artificial haptic sensory systems in rehabilitative aids and in various other human computer interfaces enhances the dexterity. This paper presents a novel approach of shape reconstruction and classification from the tactile images by touching the surface of various real life objects. Here four objects (viz. a planar surface, object with one edge, a cuboid i.e. object with two edges and a cylindrical object) have been used for the shape recognition purpose. A new gradient based feature extraction technique has been used for the classification purpose. The reconstruction algorithm also uses image gradients to differentiate between a surface having continuous curvature and a surface having sharp edge. Prewitt masks are used for determining the gradients. A comparison between the performances of different classifiers has been drawn to prove the efficacy of the shape classification algorithm.