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dc.contributor.authorChandraratne, Meegalla R.en
dc.contributor.authorSamarasinghe, Sandhyaen
dc.contributor.authorKulasiri, Gamalathge D.en
dc.contributor.authorFrampton, C.en
dc.contributor.authorBickerstaffe, Royen
dc.date.accessioned2008-01-11T02:14:42Z
dc.date.issued2003-08en
dc.identifier.citation2003, pp. 1 - 18en
dc.identifier.citation2003, pp. 1 - 18en
dc.identifier.citation2003, pp. 1 - 18en
dc.identifier.citation2003, pp. 1 - 18en
dc.identifier.citation2003, pp. 1 - 18en
dc.identifier.issn1174-6696en
dc.identifier.urihttps://hdl.handle.net/10182/244
dc.description.abstractIn this study, the ability of neural network models for lamb carcass classification was compared with a multivariate statistical technique with respect to the classification accuracy. The lamb carcass classification system is based on image and texture analyses. Digital images of lamb chops were used to calculate twelve image geometric variables. In addition, a set of ninety textural features was used to extract the textural information from the acquired images. Texture analysis is based on the grey level co-occurrence matrix method. Principal component analysis (PCA) was used to reduce the dimensionality of feature spaces. Two feature sets were generated. These feature sets comprised of 14 principal component (PC) scores calculated from the original variables and 14 variables selected from the original set of variables. Both feature spaces were used for neural network and discriminant analysis. Several network configurations were tested and the classification accuracy of 93% was achieved from three-layer multilayer perceptron (MLP) network. Its performance was 14% better than that from the Discriminant function analysis (DFA). The study shows the predictive potential of combining neural networks with texture analysis for lamb grading.en
dc.format.extent1-18en
dc.language.isoenen
dc.publisherLincoln University. Applied Computing, Mathematics and Statistics Groupen
dc.relationThe original publication is available from - Lincoln University. Applied Computing, Mathematics and Statistics Group - http://hdl.handle.net/10182/244en
dc.relation.hasversionThe original publication is available from Lincoln University. Applied Computing, Mathematics and Statistics Group.
dc.relation.hasversionThe original publication is available from Lincoln University. Applied Computing, Mathematics and Statistics Group, or from http://hdl.handle.net/10182/244
dc.relation.hasversionThe original publication is available from Lincoln University. Applied Computing, Mathematics and Statistics Group, or from http://hdl.handle.net/10182/244en
dc.subjectlamben
dc.subjectlamb gradingen
dc.subjectimage analysisen
dc.subjecttexture featuresen
dc.subjectcomputer visionen
dc.subjectdiscriminant function analysisen
dc.subjectartificial neural networksen
dc.subjectcooccurrence matrixen
dc.subjectmeat textureen
dc.titleLamb carcass classification system based on computer vision. Part 2, Texture features and neural networksen
dc.typeOther
dc.subject.marsdenFields of Research::280000 Information, Computing and Communication Sciences::280200 Artificial Intelligence and Signal and Image Processingen
dc.subject.marsdenFields of Research::290000 Engineering and Technology::290100 Industrial Biotechnology and Food Sciencesen
lu.contributor.unitLincoln Universityen
lu.contributor.unitFaculty of Agriculture and Life Sciencesen
lu.contributor.unit/LU/Agriculture and Life Sciences/CELLen
lu.contributor.unitDepartment of Wine, Food and Molecular Biosciencesen
lu.contributor.unitFaculty of Environment, Society and Designen
lu.contributor.unitDepartment of Environmental Managementen
lu.contributor.uniten
lu.contributor.uniten
dc.subject.anzsrc140201 Agricultural Economicsen
pubs.confidentialfalseen
pubs.notesInternal Research Report, Applied Management and Computing Division, LIncoln Universityen
pubs.organisational-group/LU
pubs.organisational-group/LU/Agriculture and Life Sciences
pubs.organisational-group/LU/Agriculture and Life Sciences/CELL
pubs.organisational-group/LU/Agriculture and Life Sciences/WFMB
pubs.organisational-group/LU/Faculty of Environment, Society and Design
pubs.organisational-group/LU/Faculty of Environment, Society and Design/DEM
pubs.organisational-group/LU/Research Management Office
pubs.organisational-group/LU/Research Management Office/2018 PBRF Staff group
pubs.publication-statusPublisheden
pubs.publisher-urlhttp://hdl.handle.net/10182/244en
dc.publisher.placeLincoln, Canterburyen
lu.identifier.orcid0000-0001-8744-1578
lu.identifier.orcid0000-0003-2943-4331


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