{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/21338"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/21338","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A knowledge-based machine vision system for grain quality inspection","abstract":"A knowledge-based machine vision system was developed for automatic corn quality inspection. This system consisted of a primitive feature extraction algorithm, several quality-related feature extraction algorithms, and several knowledge-based corn quality inspection algorithms. The feature extraction and corn quality inspection algorithms were developed and their performance evaluated.","abstract_html":"A knowledge-based machine vision system was developed for automatic corn quality inspection. This system consisted of a primitive feature extraction algorithm, several quality-related feature extraction algorithms, and several knowledge-based corn quality inspection algorithms. The feature extraction and corn quality inspection algorithms were developed and their performance evaluated.","abstract_has_math":false,"creators":["Liao, Ke"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Agricultural Engineering","degree_department":null,"school":null,"contributors":["Paulsen, Marvin R."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-05-07T13:05:45Z","date_published":"2011-05-07T13:05:45Z","updated_at":"2026-07-22T22:25:17Z","subjects":["Agriculture, Food Science and Technology","Engineering, Agricultural","Artificial Intelligence"],"languages":["eng"],"rights":["Copyright 1993 Liao, Ke"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["AAI9329098","(UMI)AAI9329098"],"render_values":[{"text":"AAI9329098","href":null,"code":true},{"text":"(UMI)AAI9329098","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/21338","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Paulsen, Marvin R."]},{"key":"dc:creator","label":"Author","values":["Liao, Ke"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2011-05-07T13:05:45Z","10000-01-01","1993"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Agricultural Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Agriculture, Food Science and Technology","Engineering, Agricultural","Artificial Intelligence"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 1993 Liao, Ke"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["AAI9329098","(UMI)AAI9329098","http://hdl.handle.net/2142/21338"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["A knowledge-based machine vision system was developed for automatic corn quality inspection. This system consisted of a primitive feature extraction algorithm, several quality-related feature extraction algorithms, and several knowledge-based corn quality inspection algorithms. The feature extraction and corn quality inspection algorithms were developed and their performance evaluated.","The primitive feature extraction algorithm was developed using on-board hardware-based operations. The primitive features were computed in a processing time of less than one second for one object. The quality-related feature extraction algorithms were developed based on the results of the primitive feature extraction algorithm. A geometric dimension measurement algorithm was evaluated. An average color measurement algorithm was used to separate white and yellow corn varieties. The processing time was about 1.3 seconds.","The knowledge-based quality inspection algorithms were developed by training with pre-classified corn samples using knowledge acquisition algorithms. The pericarp damage inspection algorithm provided a successful classification of 95, 80, and 93% for negligible, minor, and severe damage, respectively. The processing time for the pericarp damage inspection program was about 1.0 to 2.5 seconds.","A Fourier profile-based kernel breakage inspection algorithm had an accuracy of 95% for classifying whole kernels as whole and 96% for classifying broken kernels as broken. The processing time of the breakage inspection program was about 1.5 seconds.","A morphological, curvature/symmetry, profile-based kernel breakage inspection algorithm provided a successful classification of 94 and 95% for whole and broken kernels. The processing time for the classification required about 1.5 seconds from grabbing the live image to the final classification result. The software-based neural network classifier required about 0.2 second of the 1.5 second total time.","The RGB and multispectral image-based color discrimination algorithms were also developed and evaluated by separating the color regions of vitreous endosperm, floury endosperm, germ, and red streak areas on white and on yellow corn. The color discrimination functions provided a successful off-line classification rate from 90 to 100% for the color regions of white and yellow corn kernels. The six-band multispectral images recovered more information about the variation of the spectral reflectance of corn kernels than the standard RGB images.","Made available in DSpace on 2011-05-07T13:05:45Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9329098.pdf: 4609996 bytes, checksum: 54a50798b437af35cdfd10a65acd6065 (MD5) Previous issue date: 1993","Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T14:50:06Z Item is restricted indefinitely.","Restriction data tranferred 2014-07-01T11:22:51-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission","ETDs are only available to UIUC Users without author permission","U of I Only"]},{"key":"dc:title","label":"Title","values":["A knowledge-based machine vision system for grain quality inspection"]}]}],"canonical_facts":{"dc:contributor":["Paulsen, Marvin R."],"dc:creator":["Liao, Ke"],"dc:date":["2011-05-07T13:05:45Z","10000-01-01","1993"],"dc:description":["A knowledge-based machine vision system was developed for automatic corn quality inspection. This system consisted of a primitive feature extraction algorithm, several quality-related feature extraction algorithms, and several knowledge-based corn quality inspection algorithms. The feature extraction and corn quality inspection algorithms were developed and their performance evaluated.","The primitive feature extraction algorithm was developed using on-board hardware-based operations. The primitive features were computed in a processing time of less than one second for one object. The quality-related feature extraction algorithms were developed based on the results of the primitive feature extraction algorithm. A geometric dimension measurement algorithm was evaluated. An average color measurement algorithm was used to separate white and yellow corn varieties. The processing time was about 1.3 seconds.","The knowledge-based quality inspection algorithms were developed by training with pre-classified corn samples using knowledge acquisition algorithms. The pericarp damage inspection algorithm provided a successful classification of 95, 80, and 93% for negligible, minor, and severe damage, respectively. The processing time for the pericarp damage inspection program was about 1.0 to 2.5 seconds.","A Fourier profile-based kernel breakage inspection algorithm had an accuracy of 95% for classifying whole kernels as whole and 96% for classifying broken kernels as broken. The processing time of the breakage inspection program was about 1.5 seconds.","A morphological, curvature/symmetry, profile-based kernel breakage inspection algorithm provided a successful classification of 94 and 95% for whole and broken kernels. The processing time for the classification required about 1.5 seconds from grabbing the live image to the final classification result. The software-based neural network classifier required about 0.2 second of the 1.5 second total time.","The RGB and multispectral image-based color discrimination algorithms were also developed and evaluated by separating the color regions of vitreous endosperm, floury endosperm, germ, and red streak areas on white and on yellow corn. The color discrimination functions provided a successful off-line classification rate from 90 to 100% for the color regions of white and yellow corn kernels. The six-band multispectral images recovered more information about the variation of the spectral reflectance of corn kernels than the standard RGB images.","Made available in DSpace on 2011-05-07T13:05:45Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9329098.pdf: 4609996 bytes, checksum: 54a50798b437af35cdfd10a65acd6065 (MD5) Previous issue date: 1993","Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T14:50:06Z Item is restricted indefinitely.","Restriction data tranferred 2014-07-01T11:22:51-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission","ETDs are only available to UIUC Users without author permission","U of I Only"],"dc:identifier":["AAI9329098","(UMI)AAI9329098","http://hdl.handle.net/2142/21338"],"dc:language":["eng"],"dc:rights":["Copyright 1993 Liao, Ke"],"dc:subject":["Agriculture, Food Science and Technology","Engineering, Agricultural","Artificial Intelligence"],"dc:title":["A knowledge-based machine vision system for grain quality inspection"],"dc:type":["text"],"thesis:degree_discipline":["Agricultural Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:17Z"}