Title :
INSPECTOR: A Computer Vision System that Learns to Inspect Parts
Author_Institution :
Computer Science Department, General Motors Research Laboratories, Warren, MI 49090; Lockheed Palo Alto Research Laboratory, Palo Alto, CA 94304.
Abstract :
A computer vision inspection system has been developed that learns the difference between good and bad parts by being shown several identified good and bad parts. The model, formed during the training session, contains identifying points which are used for locating parts and inspection tests which apply only to pertinent regions of the part. Using the model, the system can distinguish between good parts and bad parts with an arbitrary number of defects. It can also learn to classify parts if it is shown the different parts during the training session. Examples of INSPECTOR inspecting industrial parts are shown.
Keywords :
Cameras; Computer vision; Gray-scale; Industrial training; Inspection; Laboratories; Pattern classification; Pattern recognition; TV; Testing; Automatic inspection; inspection; learning; part registration; pattern classification; visual learning;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.1983.4767447
