- Library Home /
- Search Collections /
- Open Collections /
- Browse Collections /
- UBC Theses and Dissertations /
- Identification of salmon can-filling defects using...
Open Collections
UBC Theses and Dissertations
UBC Theses and Dissertations
Identification of salmon can-filling defects using machine vision O’Dor, Matthew Arnold
Abstract
During the salmon can-filling process, a number of can-filling defects can result from the incorrect insertion of the salmon meat into the cans. These can-filling defects must be repaired before sealing the cans. Thus, in the existing industrial process, every can is manually inspected to identify the defective cans. This thesis details a research project on the use of machine vision for the inspection of filled cans of salmon. The types of can-filling defects were identified and defined through consultations with salmon canning quality assurance experts. Images of can-filling defects were acquired at a production facility. These images were examined and feature extraction algorithms were developed to extract the features necessary for the identification of two types of can-filling defects. Radial basis function networks and fuzzy logic methods for classifying the extracted features were developed. These classification methods are evaluated and compared. A research prototype was designed and constructed to evaluate the machine vision algorithms on-line.
Item Metadata
Title |
Identification of salmon can-filling defects using machine vision
|
Creator | |
Publisher |
University of British Columbia
|
Date Issued |
1998
|
Description |
During the salmon can-filling process, a number of can-filling defects can result from the incorrect
insertion of the salmon meat into the cans. These can-filling defects must be repaired before
sealing the cans. Thus, in the existing industrial process, every can is manually inspected to
identify the defective cans. This thesis details a research project on the use of machine vision
for the inspection of filled cans of salmon. The types of can-filling defects were identified and
defined through consultations with salmon canning quality assurance experts. Images of can-filling
defects were acquired at a production facility. These images were examined and feature extraction
algorithms were developed to extract the features necessary for the identification of two types
of can-filling defects. Radial basis function networks and fuzzy logic methods for classifying the
extracted features were developed. These classification methods are evaluated and compared. A
research prototype was designed and constructed to evaluate the machine vision algorithms on-line.
|
Extent |
22023773 bytes
|
Genre | |
Type | |
File Format |
application/pdf
|
Language |
eng
|
Date Available |
2009-04-30
|
Provider |
Vancouver : University of British Columbia Library
|
Rights |
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.
|
DOI |
10.14288/1.0080877
|
URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
|
Graduation Date |
1998-05
|
Campus | |
Scholarly Level |
Graduate
|
Aggregated Source Repository |
DSpace
|
Item Media
Item Citations and Data
Rights
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.