Full metadata record
DC FieldValueLanguage
dc.contributor.authorChang, JYen_US
dc.contributor.authorCho, CWen_US
dc.date.accessioned2014-12-08T15:25:16Z-
dc.date.available2014-12-08T15:25:16Z-
dc.date.issued2005en_US
dc.identifier.isbn0-7803-9048-2en_US
dc.identifier.issn1098-7576en_US
dc.identifier.urihttp://hdl.handle.net/11536/17638-
dc.description.abstractIn this paper, we utilize a neural network based machine learning algorithm to segment natural objects in outdoor scene images. We have developed a genetic algorithm based fuzzy ID3 method, which can build a fuzzy decision tree to summarize the regularities existing in the data set. Using this method, we then propose a road scene analysis system, by which natural element segmentation rules can be learned from several road scene images. In the image analysis phase, the natural element regions are obtained through inference on these learned rules. Moreover, we can apply image groundtruthing to further improve the classification accuracy. The testing results have demonstrated that the object segmentation accuracy is quite high.en_US
dc.language.isoen_USen_US
dc.titleA new scene analysis using genetic algorithm based fuzzy ID3 methoden_US
dc.typeProceedings Paperen_US
dc.identifier.journalProceedings of the International Joint Conference on Neural Networks (IJCNN), Vols 1-5en_US
dc.citation.spage1770en_US
dc.citation.epage1775en_US
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.identifier.wosnumberWOS:000235178002081-
Appears in Collections:Conferences Paper