NDLI: 3D Discrete Spherical Fourier Descriptors Based on Surface Curvature Voxels for Pollen Classification

Content Provider	IEEE Xplore Digital Library
Author	Yonghua Xie OhEigeartaigh, M.
Copyright Year	2010
Abstract	This paper presents a new method to extract 3D Discrete Spherical Fourier Descriptors (DSFD) based on surface curvature voxels for pollen recognition. In order to reduce the high amount of pollen information and noise disturbance, the geometric normalized curvature voxels with the principal curvedness are firstly extracted to represent the intrinsic pollen volumetric data. Then the curvature voxels are decomposed into radial and angular components with Spherical Harmonic Transform in spherical coordinates. Finally the discrete 3D Fourier transform is applied on the decomposed curvature voxels to obtain the 3D Spherical Fourier Descriptors for pollen recognition. Experimental results show that the presented descriptors are invariant to pollen image rotation, scale and translation, and can bring about good recognition precision and speed simultaneously.
Starting Page	207
Ending Page	211
File Size	756259
Page Count	5
File Format	PDF
ISBN	9781424475063
e-ISBN	9781424475070
DOI	10.1109/ICIE.2010.56
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image recognition Microscopy discrete 3D Fourier transform Feature extraction Harmonic analysis pollen recognition Three dimensional displays Pattern recognition curvature voxels spherical coordinates Surface treatment
Content Type	Text
Resource Type	Article

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