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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chetima, M.M. Payeur, P. |
| Copyright Year | 2012 |
| Description | Author affiliation: School of Electrical Engineering and Computer Science, University of Ottawa, Canada (Chetima, M.M.; Payeur, P.) |
| Abstract | Quality control in industrial food manufacturing can be reliably performed with computer vision systems that operate at high speed. However, most of these inspection stations need to be tuned manually and only perform well on a specific product. This research integrates machine learning techniques in the process to automate the initial tuning of real-time vision-based inspection systems for bakery products. The combination of feature selection techniques with machine learning is assessed in terms of classification performance. A formal automated tuning methodology is introduced and evaluated experimentally with data from industrial inspection stations. The work demonstrates that an inspection system automatically tuned with the proposed technique can systematically achieve 98% correct classification when compared with the classification generated with a manually tuned system. |
| Starting Page | 210 |
| Ending Page | 215 |
| File Size | 862611 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781457717734 |
| ISSN | 10915281 |
| e-ISBN | 9781457717727 |
| DOI | 10.1109/I2MTC.2012.6229334 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-05-13 |
| Publisher Place | Austria |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Inspection Accuracy Decision trees Tuning Machine learning Feature extraction Training automated tuning Food inspection quality control machine vision machine learning feature selection |
| Content Type | Text |
| Resource Type | Article |
| Subject | Instrumentation Electrical and Electronic Engineering |
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