Journal of the Japan Society for Precision Engineering
Online ISSN : 1882-675X
Print ISSN : 0912-0289
ISSN-L : 0912-0289
Paper
Sparse FIND: A Novel Low Computational Cost Feature for Object Classification
Takeo KATOKiyosumi KIDONOYoshiko KOJIMATakashi NAITO
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JOURNAL FREE ACCESS

2013 Volume 79 Issue 11 Pages 1063-1068

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Abstract
In this paper, a novel image feature named Sparse FIND which is suitable for onboard pedestrian sensor due to having low computational cost is introduced. Histograms of Oriented Gradients (HOG) have been shown excellent capability of image classification. Feature correlation descriptor (FIND) which has achieved higher capability of image classification in comparison with HOG has been developed. However, FIND requires much more computational cost than HOG due to computing the correlation between histogram elements. In Sparse FIND, the computational cost is reduced by limiting the computation of the correlations between histogram elements which contribute classification majorly. Through experiments, Sparse FIND performs nearly the same capability of image classification as FIND does despite the computational cost is approximately one tenth in comparison with it of FIND, is confirmed.
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© 2013 The Japan Society for Precision Engineering
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