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dc.contributor.authorDing, Yuhua
dc.contributor.authorVachtsevanos, George J.
dc.contributor.authorYezzi, Anthony
dc.contributor.authorZhang, Yingchuan
dc.contributor.authorWardi, Yorai
dc.date.accessioned2013-10-08T15:39:44Z
dc.date.available2013-10-08T15:39:44Z
dc.date.issued2002
dc.identifier.citationDing, Y.; Vachtsevanos, G.J.; Yezzi, A.J.; Zhang, Y.; & Wardi, Y. (2002). “A Recursive Segmentation and Classification Scheme for Improving Segmentation Accuracy and Detection Rate in Real-time Machine Vision Applications”. 14th International Conference on Digital Signal Processing (DSP 2002), Vol. 2, pp.1009-1013.en_US
dc.identifier.isbn0-7803-7503-3
dc.identifier.urihttp://hdl.handle.net/1853/49193
dc.description© 2002 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.descriptionDOI: 10.1109/ICDSP.2002.1028261
dc.description.abstractSegmentation accuracy is shown to be a critical factor in detection rate improvement. With accurate segmentation, results are easier to interpret, and classification performance is better. Therefore, it is required to have a performance measure for segmentation evaluation. However, a number of restrictions limit using existing segmentation performance measures. In this paper a recursive segmentation and classification scheme is proposed to improve segmentation accuracy and classification performance in real-time machine vision applications. In this scheme, the confidence level of classification results is used as a new performance measure to evaluate the accuracy of segmentation algorithm. Segmentation is repeated until a classification with desired confidence level is achieved. This scheme can be implemented automatically. Experimental results show that it is efficient to improve segmentation accuracy and the overall detection performance, especially for real-time machine vision applications, where the scene is complicated and a single segmentation algorithm cannot produce satisfactory results.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectClassification schemeen_US
dc.subjectImage segmentationen_US
dc.subjectReal-time machine vision applicationsen_US
dc.subjectRecursive segmentationen_US
dc.subjectSegmentation performance measuresen_US
dc.titleA Recursive Segmentation and Classification Scheme for Improving Segmentation Accuracy and Detection Rate in Real-time Machine Vision Applicationsen_US
dc.typeProceedingsen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Electrical and Computer Engineeringen_US
dc.publisher.originalInstitute of Electrical and Electronics Engineers
dc.identifier.doi10.1109/ICDSP.2002.1028261
dc.embargo.termsnullen_US


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