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

Yaghoubyan, S. Hadi and Maarof, Mohd Aizaini and Zainal, Anazida and Kiani, M. J. and Rad, Farhad and Maktabdar Oghaz, Mahdi (2016) A Robust Keypoint Descriptor Based on Tomographic Image Reconstruction Using Heuristic Genetic Algorithm and Principal Component Analysis Techniques. Journal of Computational and Theoretical Nanoscience, 13 (8). pp. 5554-5568. ISSN 1546-1955

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Official URL: https://doi.org/10.1166/jctn.2016.5453

Abstract

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.

Item Type: Journal Article
Keywords: Feature Descriptor, Image Patch, Keypoint, Sensitivity Map, Terminal Point, Tomography-Based Descriptor
Faculty: ARCHIVED Faculty of Science & Technology (until September 2018)
Depositing User: Lisa Blanshard
Date Deposited: 09 Mar 2020 15:43
Last Modified: 09 Sep 2021 16:13
URI: https://arro.anglia.ac.uk/id/eprint/705268

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