Specifications

time semi-discretization and full discretization schemes are both established strictly for the two
schemes. Both of them are unconditionally stable. Numerically the convergent orders in space
(including the solution and its first derivative) are four for the Hermite cubic spline approximation,
and theoretically we get that at least the solution itself has a fourth order convergent rate.
Extensive numerical results are presented to show the convergent order and robustness of the
numerical schemes. © 2013 Elsevier B.V. All rights reserved.
Number of references: 31
Main heading: Partial differential equations
Controlled terms: Convergence of numerical methods - Polynomials
Uncontrolled terms: Convergence - Fractional derivatives - Full
discretization - Hermite cubic splines - Orthogonal splines - Stability and
convergence - Subdiffusion equations - Unconditionally stable
Classification code: 921 Mathematics
DOI: 10.1016/j.cam.2013.05.022
Database: Compendex
Compilation and indexing terms, © 2013 Elsevier Inc.
9.
Accession number: 20132716471653
Title: Image matching based on improved SIFT algorithm
Authors: Liu, Jia1, 2 ; Fu, Weiping1 ; Wang, Wen1 ; Li, Na1/刘佳;傅卫平;王雯;李娜
Author affiliation:
1 School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology,
Xi'an 710048, China
2 School of Science, Xi'an University of Science and Technology, Xi'an 710054, China
Corresponding author: Liu, J. ([e-mail])
Source title: Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument
Abbreviated source title: Yi Qi Yi Biao Xue Bao
Volume: 34
Issue: 5
Issue date: May 2013
Publication year: 2013
Pages: 1107-1112
Language: Chinese
ISSN: 02543087
CODEN: YYXUDY
Document type: Journal article (JA)
Publisher: Science Press, 18,Shuangqing Street,Haidian, Beijing, 100085, China
Abstract: In order to further improve the robustness and accuracy of SIFT matching
algorithm, the SIFT algorithm is improved in the following several aspects. Multi-resolution
wavelet transform is performed on the images, the image approximation components that are
reconstructed-low-frequency information is adopted to match the images; and a "nested
box"-shaped double square neighborhood window is used to divide the neighborhood of a
feature point into four areas and construct a 32 dimension feature descriptor vector. Euclidean
distance is used to preliminarily ensure the matching points, and then integral image is used to