Portrayal Matching Algorithm By using Sift
D. Praveena Bai1, K. Kezia Chrysolite2, B. Bharathi3, K. Saisudha4, B. Bhavani5
1D. Praveena Bai, Assistant Professor, Department of Electronics and Communication, P. V. P Siddhartha Institute of Technology, Vijayawada, India.
2K. Kezia Chrysolite, Department of Electronics and Communication, P. V. P Siddhartha Institute of Technology, Vijayawada, India.
3B. Bharathi, Department of Electronics and Communication, P. V. P Siddhartha Institute of Technology, Vijayawada, India.
4K.Sai Sudha, Department of Electronics and Communication, P. V. P Siddhartha Institute of Technology, Vijayawada, India.
5B. Bhavani, Department of Electronics and Communication, P. V. P Siddhartha Institute of Technology, Vijayawada, India.
Manuscript received on May 02, 2020. | Revised Manuscript received on May 21, 2020. | Manuscript published on May 30, 2020. | PP: 2711-2713 | Volume-9 Issue-1, May 2020. | Retrieval Number: A2719059120/2020©BEIESP | DOI: 10.35940/ijrte.A2719.059120
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Image identification and matching is one of the very difficult assignment in different areas of mainframe view. Scale-Invariant Feature Transform is an algorithm to perceive and represent specific features in portryals to further use them as an image matching criteria. In this paper, the extracted SIFT matching features are against various image distortions such as rotation, scaling, fisheye and motion distortion are evaluated and false and true positive rates for a large number of image pairs are calculated and presented.
Keywords: MATLAB; SIFT; Portrayal matching; Difference of Gaussians (DOG).
Scope of the Article: Parallel and Distributed Algorithms