TY - GEN
T1 - Object-shape classification and reconstruction from tactile images using image gradient
AU - Singh, Garima
AU - Khasnobish, Anwesha
AU - Jati, Arindam
AU - Bhattacharyya, Saugat
AU - Konar, Amit
AU - Tibarewala, D. N.
AU - Janarthanan, R.
PY - 2013/1/11
Y1 - 2013/1/11
N2 - 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.
AB - 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.
KW - Feed Forward Neural Network (FFNN)
KW - k-Nearest Neighbor (kNN)
KW - Linear Discriminant Analysis (LDA)
KW - linear support vector machine (LSVM)
KW - reconstruction
KW - Shape classification
KW - Support Vector Machine with Gaussian Radial Basis Function kernel (SVM-RBF)
KW - tactile image
UR - http://www.scopus.com/inward/record.url?scp=84873497049&partnerID=8YFLogxK
U2 - 10.1109/EAIT.2012.6407870
DO - 10.1109/EAIT.2012.6407870
M3 - Conference contribution
AN - SCOPUS:84873497049
SN - 9781467318259
T3 - Proceedings - 2012 3rd International Conference on Emerging Applications of Information Technology, EAIT 2012
SP - 93
EP - 96
BT - Proceedings - 2012 3rd International Conference on Emerging Applications of Information Technology, EAIT 2012
PB - IEEE
T2 - 2012 3rd International Conference on Emerging Applications of Information Technology, EAIT 2012
Y2 - 30 November 2012 through 1 December 2012
ER -
