A Recursive Segmentation and Classification Scheme for Improving Segmentation Accuracy and Detection Rate in Real-time Machine Vision Applications

Ding, Yuhua; Vachtsevanos, George J.; Yezzi, Anthony; Zhang, Yingchuan; Wardi, Yorai Y.

Title:

A Recursive Segmentation and Classification Scheme for Improving Segmentation Accuracy and Detection Rate in Real-time Machine Vision Applications

dc.contributor.author	Ding, Yuhua
dc.contributor.author	Vachtsevanos, George J.
dc.contributor.author	Yezzi, Anthony
dc.contributor.author	Zhang, Yingchuan
dc.contributor.author	Wardi, Yorai Y.
dc.contributor.corporatename	Georgia Institute of Technology. School of Electrical and Computer Engineering	en_US
dc.date.accessioned	2013-10-08T15:39:44Z
dc.date.available	2013-10-08T15:39:44Z
dc.date.issued	2002
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.description	DOI: 10.1109/ICDSP.2002.1028261
dc.description.abstract	Segmentation 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.embargo.terms	null	en_US
dc.identifier.citation	Ding, 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.doi	10.1109/ICDSP.2002.1028261
dc.identifier.isbn	0-7803-7503-3
dc.identifier.uri	http://hdl.handle.net/1853/49193
dc.language.iso	en_US	en_US
dc.publisher	Georgia Institute of Technology	en_US
dc.publisher.original	Institute of Electrical and Electronics Engineers
dc.subject	Classification scheme	en_US
dc.subject	Image segmentation	en_US
dc.subject	Real-time machine vision applications	en_US
dc.subject	Recursive segmentation	en_US
dc.subject	Segmentation performance measures	en_US
dc.title	A Recursive Segmentation and Classification Scheme for Improving Segmentation Accuracy and Detection Rate in Real-time Machine Vision Applications	en_US
dc.type	Text
dc.type.genre	Proceedings
dspace.entity.type	Publication
local.contributor.author	Yezzi, Anthony
local.contributor.author	Wardi, Yorai Y.
local.contributor.author	Vachtsevanos, George J.
local.contributor.corporatename	School of Electrical and Computer Engineering
local.contributor.corporatename	College of Engineering
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