Hostname: page-component-8448b6f56d-xtgtn Total loading time: 0 Render date: 2024-04-24T00:22:56.256Z Has data issue: false hasContentIssue false

Tactile image computation using a feature extraction algorithm

Published online by Cambridge University Press:  09 March 2009

M. Mehdian
Affiliation:
Robotics and Machine Intelligence Group, School of Engineering, Thames Polytechnic, Woolwich, London SE18 6PF (UK)

Summary

A binary tactile image feature extraction algorithm using image primitive notation and perceptrons is presented. The basic image segments are defined as geometric factors by which the image structure is described so that effective feature values such as image shape, image size, perimeter and texture may be extracted on the basis of local image computation. The local property of the tactile image computation is evaluated by the concept called order of the perceptrons and based on this feature extraction algorithm, an efficient tactile image recognition system is realised.

Type
Article
Copyright
Copyright © Cambridge University Press 1992

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Mehdian, M. and Rahnejat, H., “A Tactile Sensor with Automatic Learning Capability for Industrial Parts InspectionInt. J. Advanced Manufacturing Technology, 2(4), 1126(1987).CrossRefGoogle Scholar
2.Kinoshita, G.I. and Hattori, K. “Tactile Sensor Design and Tactile Sensing on 3-D Objects” International Symposium on Design and Synthesis, Tokyo, (1984) pp. 657662.Google Scholar
3.Ozaki, H., Waku, S. and Mohri, A.Pattern Recognition of a Grasped Object by Unit Vector DistributionIEEE Trans. on System, Man and Cybernetics 12(3), 315324 (1982).Google Scholar
4.Cameron, A.Optical Tactile Sensor PlacementIEEE International Conference on Robotics and Automation 1, 308313 (1989).Google Scholar
5.Overton, K.J. and Williams, T.Tactile Sensation for RobotsProceedings of the 7th International Joint Conference on Artificial Intelligence,Vancover,Canada,August (1981) pp. 162169.Google Scholar
6.Tise, B., “A Compact High Resolution Pieroresistive Digital Tactile Sensor” IEEE International Conference on Robotics and Automation 760764 (1988).Google Scholar
7.Mehdian, M. and Rahnejat, H.A Sensory Gripper using Tactile Sensors for Object Recognition, Orientation Control and Stable ManipulationIEEE Trans. on Systems, Man and Cybernetics 19, No. 5, 12501261 (1989).Google Scholar
8.Bastuschek, C.M., “Area Touch Sensor for Dexterous Manipulation”. IEEE International Conference on Robot ics and Automation 1, 151156 (1989).Google Scholar
9.Mehdian, M., Hall, A.R. and Rahnejat, H., “Object Recognition with Tactile Sensing using an Intelligent Local-Feature-Focus MethodologyInt. J. Advanced Manufacturing Technology, 5 165174 (1990).Google Scholar
10.Fearing, R.S. and Binford, T.O., “Using a Cylindrical Tactile Sensor for Determining Curvature” IEEE International Conference on Robotics and Automation 765771 (1988).Google Scholar
11.Firschein, O. and Fischler, D.M.A., “Describing and Abstracting Pictorial StructurePattern Recognition 3, 421–43 (1971).CrossRefGoogle Scholar
12.Hayashi, Y. and Sato, Y., “A Feature Extraction Algorithm based on Local Picture ComputationIEEE Transaction on Systems, Man and Cybernetics 7(10), 743–49 (1977).Google Scholar
13.Freeman, H., “On the Encoding of Arbitrary Geometric Configurations”. IRE Transactions on Elect. Comput., Vol. EC-10, pp 260–68, 1961.Google Scholar
14.Dougherty, E.R. and Giardina, C.R., Morphological Methods in Image and Signal Processing (Prentice-Hall, Englewoods Cliff, NJ, 1987).Google Scholar
15.Rosenfeld, A. and Kak, A.C., Digital Picture Processing 182, 2nd Edition (Academic Press, New York, 1982).Google Scholar
16.Minsky, M.L. and Rapert, S.A.Perceptrons (MIT Press, Cambridge, Mass., 1988).Google Scholar