NDLI: Motion-blurred SIFT invariants based on sampling in image deformation space and univariate search

Content Provider	IET Digital Library
Author	Fu, Bo Guo, Hao Zhao, Xilin Chang, Yufang Li, Bo He, Li
Abstract	Scale-invariant feature transform (SIFT) operator is a widely used algorithm to detect local features in images, yet applying this algorithm for motion-blurred image matching is difficult and inefficient. To resolve this issue, this study presents a motion-blurred invariant SIFT algorithm that is based on sample matching in an image deformation reconstructed space. First, the motion-blurred equation is deduced and its controlling parameters to reconstruct the deformation space are discretised. Second, the authors matched samples in the motion-blurred space with the blurred image to identify maximal matching points and optimal parameters. In order to improve the searching efficiency, the authors used a univariate search technique combined with a variable step hill-climbing method to determine the optimal matching. Together, the experimental results show that this improved algorithm has superior matching performance for motion-blurred images.
Starting Page	709
Ending Page	717
Page Count	9
ISSN	17519632
Volume Number	10
e-ISSN	17519640
Issue Number	Issue 7, Oct (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/10/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2015.0076
Journal	IET Computer Vision
Publisher Date	2016-04-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Combinatorial Mathematics Computer Vision And Image Processing Technique Feature Extraction Image Deformation Reconstructed Space Image Deformation Space Sampling Image Matching Image Motion Analysis Image Recognition Image Reconstruction Image Restoration Image Sampling Integral Transforms Local Feature Detection Maximal Matching Point Identification Motion-blurred Image Matching Motion-blurred SIFT Invariants Optimisation Technique Scale-invariant Feature Transform Operator Search Problem Transform Univariate Search Technique Variable Step Hill-climbing Method
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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