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

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dc.contributor.authorBulugu, Isack
dc.contributor.authorYe, ZhongFu
dc.date.accessioned2016-09-16T12:54:09Z
dc.date.available2016-09-16T12:54:09Z
dc.date.issued2016-02
dc.description.abstractThis 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 ExtendedHLAC can averaged reach 94.63% higher than using HLAC features on a Intel Core i5-4590 CPU running at 3.3 GHz.en_US
dc.identifier.citationBulugu, I. and Ye, Z., 2016. Scale Invariant static hand-postures detection using Extended Higher-order Local Autocorrelation features. International Journal of Computer Applications, 135(5), pp.1-5.en_US
dc.identifier.doi10.5120/ijca2016904742
dc.identifier.urihttp://hdl.handle.net/20.500.11810/3719
dc.language.isoenen_US
dc.publisherFoundation of Computer Scienceen_US
dc.subjectScale invarianten_US
dc.subjectLog polar imageen_US
dc.subjectPosture detectionen_US
dc.subjectPosture classificationen_US
dc.titleScale Invariant Static Hand-postures Detection using Extended Higher-order Local Autocorrelation Featuresen_US
dc.typeJournal Article, Peer Revieweden_US
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