Directional intensified feature description using tertiary filtering for augmented reality tracking

Indhumathi S; J Christopher Clement

doi:10.1038/s41598-023-46643-6

Directional intensified feature description using tertiary filtering for augmented reality tracking

Sci Rep. 2023 Nov 20;13(1):20311. doi: 10.1038/s41598-023-46643-6.

Authors

Indhumathi S¹, J Christopher Clement²

Affiliations

¹ School of Electronics Engineering, Vellore Institute of Technology, Vellore, India.
² School of Electronics Engineering, Vellore Institute of Technology, Vellore, India. christopher.clement@vit.ac.in.

Abstract

Augmented Reality (AR) is applied in almost every field, and a few, but not limited, are engineering, medical, gaming and internet of things. The application of image tracking is inclusive in all these mentioned fields. AR uses image tracking to localize and register the position of the user/AR device for superimposing the virtual image into the real-world. In general terms, tracking the image enhance the users' experience. However, in the image tracking application, establishing the interface between virtual realm and the physical world has many shortcomings. Many tracking systems are available, but it lacks in robustness and efficiency. The robustness of the tracking algorithm, is the challenging task of implementation. This study aims to enhance the users' experience in AR by describing an image using Directional Intensified Features with Tertiary Filtering. This way of describing the features improve the robustness, which is desired in the image tracking. A feature descriptor is robust, in the sense that it does not compromise, when the image undergoes various transformations. This article, describes the features based on the Directional Intensification using Tertiary Filtering (DITF). The robustness of the algorithm is improved, because of the inherent design of Tri-ocular, Bi-ocular and Dia-ocular filters that can intensify the features in all required directions. The algorithm's robustness is verified with respect to various image transformations. The oxford dataset is used for performance analysis and validation. DITF model is designed to achieve the repeatability score of illumination-variation , blur changes and view-point variation, as 100%, 100% and 99% respectively. The comparative analysis has been performed in terms of precision and re-call. DITF outperforms the state-of-the-art descriptors, namely, BEBLID, BOOST, HOG, LBP, BRISK and AKAZE. An Implementation of DITF source code is available in the following GitHub repository: github.com/Johnchristopherclement/Directional-Intensified-Feature-Descriptor.