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Scale Invariant static hand-postures detection using Extended Higher-order Local Autocorrelation features

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 135 - Number 5
Year of Publication: 2016
Authors:
Isack Bulugu, Zhongfu Ye
10.5120/ijca2016904742

Isack Bulugu and Zhongfu Ye. Article: Scale Invariant static hand-postures detection using Extended Higher-order Local Autocorrelation features. International Journal of Computer Applications 135(5):1-5, February 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Isack Bulugu and Zhongfu Ye},
	title = {Article: Scale Invariant static hand-postures detection using Extended Higher-order Local Autocorrelation features},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {135},
	number = {5},
	pages = {1-5},
	month = {February},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

This 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.

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Keywords

Scale invariant, log polar image, posture detection, posture classification.