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Insulator recognition based on moments invariant features and Cascade AdaBoost classifier
He SY(何思远); Wang L(王玲); Xia Y(夏泳); Tang YD(唐延东)
Department机器人学研究室
Conference Name2013 2nd International Conference on Mechatronics and Control Engineering, ICMCE 2013
Conference DateAugust 28-29, 2013
Conference PlaceDalian, China
Author of SourceQueensland University of Technology; Korea Maritime University; Hong Kong Industrial Technology Research Centre; Inha University
Source PublicationApplied Mechanics and Materials
PublisherTrans Tech Publications Ltd,
Publication PlaceZurich-Durnten, Switzerland
2013
Pages362-367
Indexed ByEI ; CPCI(ISTP)
EI Accession number20134716999007
WOS IDWOS:000335121000071
Contribution Rank1
ISSN1660-9336
ISBN978-3-03785-894-3
KeywordClassification (Of Information) Median Filters Principal Component Analysis
AbstractA method based on moments invariant features and cascade AdaBoost classifier for insulator recognition is put forward to solve the problem of poor performance of insulator recognition. At first, the insulator image is preprocessed by median filtering, dilating, eroding and Otsu thresholding. Then, for the better extraction of moments invariant features, the preprocessed insulator image is tilted correctly based on PCA (Principal Component Analysis). Next, the moments invariant features are extracted and chosen to compose complex classifier in the process of training AdaBoost. Finally, the complex AdaBoost classifiers are combined in a cascade method for insulator recognition. The results of experiments demonstrate that the proposed method can recognize the insulator from complex background in the mountainous area, and it has better robustness, accuracy and validity. © (2013) Trans Tech Publications, Switzerland.
Language英语
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/13918
Collection机器人学研究室
Recommended Citation
GB/T 7714
He SY,Wang L,Xia Y,et al. Insulator recognition based on moments invariant features and Cascade AdaBoost classifier[C]//Queensland University of Technology; Korea Maritime University; Hong Kong Industrial Technology Research Centre; Inha University. Zurich-Durnten, Switzerland:Trans Tech Publications Ltd,,2013:362-367.
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