S Indhumathi, Clement J Christopher
School of Electronics Engineering, Vellore Institute of Technology, Vellore, India.
Sci Rep. 2023 Nov 20;13(1):20311. doi: 10.1038/s41598-023-46643-6.
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.
增强现实（AR）几乎应用于各个领域，其中包括但不限于工程、医学、游戏和物联网。图像跟踪的应用涵盖了上述所有领域。AR利用图像跟踪来定位和注册用户/AR设备的位置，以便将虚拟图像叠加到现实世界中。一般来说，跟踪图像可以提升用户体验。然而，在图像跟踪应用中，建立虚拟领域与物理世界之间的接口存在许多缺点。虽然有许多跟踪系统可用，但它们缺乏鲁棒性和效率。跟踪算法的鲁棒性是实施过程中的一项具有挑战性的任务。本研究旨在通过使用三级滤波的方向增强特征来描述图像，从而提升AR中的用户体验。这种描述特征的方式提高了鲁棒性，这在图像跟踪中是非常需要的。一个特征描述符之所以鲁棒，是指当图像经历各种变换时它不会受到影响。本文介绍了基于使用三级滤波的方向增强（DITF）的特征。由于三目、双目和双目滤波的固有设计可以在所有所需方向上增强特征，因此算法的鲁棒性得到了提高。该算法的鲁棒性针对各种图像变换进行了验证。牛津数据集用于性能分析和验证。DITF模型旨在分别实现光照变化、模糊变化和视点变化的重复性得分，分别为100%、100%和99%。已在精度和召回率方面进行了对比分析。DITF优于当前最先进的描述符，即BEBLID、BOOST、HOG、LBP、BRISK和AKAZE。DITF源代码的实现可在以下GitHub仓库中获取：github.com/Johnchristopherclement/Directional-Intensified-Feature-Descriptor。
