vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff1a0317000000db28030001000300 An extreme point of scale space extraction method for binary multiscale and rotation invariant local feature descriptor is studied in this paper in order to obtain a robust and fast method for local image feature descriptor. Classic local feature description algorithms often select neighborhood information of feature points which are extremes of image scale space, obtained by constructing the image pyramid using certain signal transform method. But build the image pyramid always consumes a large amount of computing and storage resources, is not conducive to the actual applications development. This paper presents a dual multiscale FAST algorithm, it does not need to build the image pyramid, but can extract feature points of scale extreme quickly. Feature points extracted by proposed method have the characteristic of multiscale and rotation Invariant and are fit to construct the local feature descriptor.
Hongwei Ying, Jiatao Song, Jinhe Wang, Xuena Qiu, Wang Wei, Zhongxiu Yang
Ningbo University of Technology, China
Features Extraction, Multiscale, Feature Descriptor, Corner Detection, Rotation Invariant
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 5 , Issue: 1 , Pages: 873-878 )
Date of Publication :
August 2014
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