Title :
A comparison of rule-based, k-nearest neighbor, and neural net classifiers for automated industrial inspection
Author :
Cho, Tai-Hoon ; Conners, R.W. ; Araman, Philip A.
Author_Institution :
Virginia Polytech. Inst. & State Univ., Blacksburg, VA, USA
fDate :
30 Sep-2 Oct 1991
Abstract :
As classifiers for use in automated industrial inspection, the rule-based, k-nearest-neighbor, and neural-network approaches are discussed. These approaches were implemented and tested for label verification in a machine vision system for hardwood lumber inspection. The test results, together with other considerations, have led to the selection of neural networks as the preferred method for doing the label verification in this machine vision system
Keywords :
automatic optical inspection; computer vision; computerised pattern recognition; industrial computer control; knowledge based systems; neural nets; automated industrial inspection; hardwood lumber inspection; k-nearest neighbor; label verification; machine vision; neural net classifiers; rule-based; Data analysis; Data mining; Focusing; Image segmentation; Inspection; Labeling; Machine vision; Neural networks; Performance evaluation; Testing;
Conference_Titel :
Developing and Managing Expert System Programs, 1991., Proceedings of the IEEE/ACM International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-8186-2250-4
DOI :
10.1109/DMESP.1991.171738
