Scale Invariant Static Hand-Postures Detection using Extended Higher-order Local Autocorrelation Features

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dc.creatorBulugu, Isack
dc.creatorYe, Zhongfu
dc.date2020-04-03T10:58:37Z
dc.date2020-04-03T10:58:37Z
dc.date2016-02-17
dc.date.accessioned2021-05-03T13:17:02Z
dc.date.available2021-05-03T13:17:02Z
dc.descriptionThis paper presents scale invariant static hand postures detection methods using extended HLAC features extractedfrom Log-Polar images. Scale changes of a handposture in an image are represented as shift in Log-Polar image. Robustness of the method is achieved through extracting spectral features from theeach row of the Log-Polar image. Linear Discriminant Analysis was used to combine features with simple classification methods in order to realize scale invariant hand postures detection and classification.The method was successful tested by performing experiment using NSU hand posture dataset images which consists 10 classes of postures, 24 samples of images per class, which are captured by the position and size of the hand within the image frame. The results showed that the detection rate using Extended-HLAC can averaged reach 94.63% higher than using HLAC features on a Intel Core i5-4590 CPU running at 3.3 GHz.
dc.identifierIEEE
dc.identifierhttp://hdl.handle.net/20.500.11810/5406
dc.identifier10.5120/ijca2016904742
dc.identifier.urihttp://hdl.handle.net/20.500.11810/5406
dc.publisherFoundation of Computer Science (FCS), NY, USA
dc.subjectImage processing,Scale invariant, Log polar image, Posture detection, Posture classification
dc.titleScale Invariant Static Hand-Postures Detection using Extended Higher-order Local Autocorrelation Features
dc.typeJournal Article, Peer Reviewed

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Department of Electronics and Telecommunications Engineering