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A Robust Keypoint Descriptor Based on Tomographic Image Reconstruction Using Heuristic Genetic Algorithm and Principal Component Analysis Techniques

journal contribution
posted on 2023-07-26, 14:55 authored by S. Hadi Yaghoubyan, Mohd Aizaini Maarof, Anazida Zainal, M. J. Kiani, Farhad Rad, Mahdi Maktab Dar Oghaz
Keypoint descriptor plays a significant role in a huge number of computer vision applications. A large amount of effort and a number of techniques are proposed in the literature which tried to build an image patch descriptor in different binary and n–n-binary spaces. Despite considerable performance of some existing techniques, there are still open problems to be resolved such as lack of enough reliability and robustness against some image distortions and transformations, especially brightness change, blur and JPEG compression. To address these issues, a keypoint descriptor which is adapted from Tomographic Image Reconstruction is proposed in this research. Convolution of predefined Gaussian smoothed sensitivity maps and associated image patch produce a matrix whose entities indicate the average intensity of the pixels at the convolved pixels in the image patch. The initial descriptor is constructed by finding the absolute differences of all possible pairs of matrix. Genetic Algorithm (GA) and Principal Component Analysis (PCA) are used to optimize this descriptor vector to its most discriminative features. Experimental result shows that the proposed descriptor outperformed some existing techniques particularly in brightness change, JPEG compression and blur while it has reasonable performance in other transformations.

History

Refereed

  • Yes

Volume

13

Issue number

8

Page range

5554-5568

Publication title

Journal of Computational and Theoretical Nanoscience

ISSN

1546-1955

Publisher

American Scientific Publishers

Language

  • other

Legacy posted date

2020-03-09

Legacy Faculty/School/Department

ARCHIVED Faculty of Science & Technology (until September 2018)

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