Scale Invariant Static Hand-postures Detection using Extended Higher-order Local Autocorrelation Features
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Date
2016-02
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Foundation of Computer Science
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 ExtendedHLAC
can averaged reach 94.63% higher than using HLAC
features on a Intel Core i5-4590 CPU running at 3.3 GHz.
Description
Keywords
Scale invariant, Log polar image, Posture detection, Posture classification
Citation
Bulugu, 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.