Browsing by Subject "Localization"

Now showing 1 - 6 of 6
  • Thumbnail Image
    Item
    Augmented Reality-Assisted Deep Reinforcement Learning-Based Model towards Industrial Training and Maintenance for NanoDrop Spectrophotometer
    (MDPI, 2023-06-29) Alatawi, Hibah; Albalawi, Nouf; Shahata, Ghadah; Aljohani, Khulud; Alhakamy, A’aeshah; Tuceryan, Mihran; Computer and Information Science, School of Science
    The use of augmented reality (AR) technology is growing in the maintenance industry because it can improve efficiency and reduce costs by providing real-time guidance and instruction to workers during repairs and maintenance tasks. AR can also assist with equipment training and visualization, allowing users to explore the equipment’s internal structure and size. The adoption of AR in maintenance is expected to increase as hardware options expand and development costs decrease. To implement AR for job aids in mobile applications, 3D spatial information and equipment details must be addressed, and calibrated using image-based or object-based tracking, which is essential for integrating 3D models with physical components. The present paper suggests a system using AR-assisted deep reinforcement learning (RL)-based model for NanoDrop Spectrophotometer training and maintenance purposes that can be used for rapid repair procedures in the Industry 4.0 (I4.0) setting. The system uses a camera to detect the target asset via feature matching, tracking techniques, and 3D modeling. Once the detection is completed, AR technologies generate clear and easily understandable instructions for the maintenance operator’s device. According to the research findings, the model’s target technique resulted in a mean reward of 1.000 and a standard deviation of 0.000. This means that all the rewards that were obtained in the given task or environment were exactly the same. The fact that the reward standard deviation is 0.000 shows that there is no variability in the outcomes.
  • Thumbnail Image
    Item
    Distributed Monocular SLAM for Indoor Map Building
    (Hindawi, 2017) Egodagamage, Ruwan; Tuceryan, Mihran; Computer and Information Science, School of Science
    Utilization and generation of indoor maps are critical elements in accurate indoor tracking. Simultaneous Localization and Mapping (SLAM) is one of the main techniques for such map generation. In SLAM an agent generates a map of an unknown environment while estimating its location in it. Ubiquitous cameras lead to monocular visual SLAM, where a camera is the only sensing device for the SLAM process. In modern applications, multiple mobile agents may be involved in the generation of such maps, thus requiring a distributed computational framework. Each agent can generate its own local map, which can then be combined into a map covering a larger area. By doing so, they can cover a given environment faster than a single agent. Furthermore, they can interact with each other in the same environment, making this framework more practical, especially for collaborative applications such as augmented reality. One of the main challenges of distributed SLAM is identifying overlapping maps, especially when relative starting positions of agents are unknown. In this paper, we are proposing a system having multiple monocular agents, with unknown relative starting positions, which generates a semidense global map of the environment.
  • Thumbnail Image
    Item
    Interleukin-18 stimulates a positive feedback loop during renal obstruction via interleukin-18 receptor
    (Elsevier, 2011-10) VanderBrink, Brian A.; Asanuma, Hiroshi; Hile, Karen; Zhang, Honji; Rink, Richard C.; Meldrum, Kirstan K.; Urology, School of Medicine
    PURPOSE: Interleukin-18 is a proinflammatory cytokine that is an important mediator of obstruction induced renal tubulointerstitial fibrosis independent of tumor necrosis factor-α and β1 activity. We hypothesized that interleukin-18 stimulates a positive feedback loop during obstruction via interleukin-18 receptor to increase interleukin-18 gene expression and protein production. MATERIALS AND METHODS: Male C57BL6 interleukin-18 receptor knockout (The Jackson Laboratory, Bar Harbor, Maine) and control wild-type mice underwent unilateral ureteral obstruction or sham operation and were sacrificed 1 week after surgery. Renal cortical tissue samples were harvested and analyzed for interleukin-18 protein by enzyme-linked immunosorbent assay, and for interleukin-18 and interleukin-18 receptor gene expression by quantitative polymerase chain reaction. The specific cellular localization of interleukin-18 and interleukin-18 receptor expression during obstruction was assessed using dual labeling immunofluorescence staining. RESULTS: Renal interleukin-18 receptor expression increased significantly in wild-type mice in response to obstruction but remained at sham operation levels in interleukin-18 receptor knockout mice. Similarly while interleukin-18 protein and gene expression were significantly increased in wild-type mice in response to obstruction, interleukin-18 levels and gene expression were significantly decreased during obstruction in knockout mice. Obstruction induced interleukin-18 and interleukin-18 receptor production were localized predominantly to tubular epithelial cells and to a lesser extent to the renal interstitium. CONCLUSIONS: Results reveal that interleukin-18 stimulates a positive feedback loop via interleukin-18 receptor during renal obstruction to stimulate interleukin-18 production and gene expression. The predominant cellular source of interleukin-18 production during renal obstruction appears to be tubular epithelial cells rather than infiltrating macrophages.
  • Thumbnail Image
    Item
    Registration and Localization of Unknown Moving Objects in Monocular SLAM
    (IEEE, 2022-03-23) Troutman, Blake; Tuceryan, Mihran; Computer and Information Science, School of Science
    Augmented reality (AR) applications require constant device localization, which is often fulfilled by visual simultaneous localization and mapping (SLAM). SLAM provides realtime camera localization by also dynamically building a 3D map of the environment, but the functionality of SLAM systems generally stops here. Useful applications of AR could make great use of additional information about the environment, such as the structure and location of moving objects in the scene (including objects that were not previously known to be separate from the static points of the map). We present an approach for solving the visual SLAM problem while also registering and localizing moving objects without prior knowledge of the objects’ structure, appearance, or existence. This is accomplished via analysis of reprojection errors and iterative use of the ePnP algorithm in a RANSAC scheme. The approach is demonstrated with the accompanying prototype system, LUMO-SLAM. The initial results achieved by this system indicate that the approach is both sound and potentially viable for some practical applications of AR and visual SLAM.
  • Thumbnail Image
    Item
    Towards Dynamic Realtime Object Labeling in Augmented Reality
    (IEEE, 2022-12) Troutman, Blake; Tuceryan, Mihran; Computer and Information Science, School of Science
    The applicability of augmented reality (AR) is stunted by the current limitations of localization systems. In various forms, simultaneous localization and mapping (SLAM) has become a common framework for providing device localization in AR systems; however, outside of camera localization data, SLAM systems typically fail to provide additional information about the environment to consumer applications. This limits the domain of potential AR applications, as many applications will require some degree of interaction between the real and virtual worlds. One such application is object labeling for moving objects. In this work, we implement an AR moving object labeling system by utilizing LUMO-SLAM, a SLAM system that registers and localizes unknown moving objects in the environment. Test runs of the system show that moving object information provided by LUMO-SLAM is sufficient for implementing a useful moving object labeling system and potentially other real-world applications of AR.
  • Thumbnail Image
    Item
    Understanding metric-related pitfalls in image analysis validation
    (ArXiv, 2023-09-25) Reinke, Annika; Tizabi, Minu D.; Baumgartner, Michael; Eisenmann, Matthias; Heckmann-Nötzel, Doreen; Kavur, A. Emre; Rädsch, Tim; Sudre, Carole H.; Acion, Laura; Antonelli, Michela; Arbel, Tal; Bakas, Spyridon; Benis, Arriel; Blaschko, Matthew B.; Buettner, Florian; Cardoso, M. Jorge; Cheplygina, Veronika; Chen, Jianxu; Christodoulou, Evangelia; Cimini, Beth A.; Collins, Gary S.; Farahani, Keyvan; Ferrer, Luciana; Galdran, Adrian; Van Ginneken, Bram; Glocker, Ben; Godau, Patrick; Haase, Robert; Hashimoto, Daniel A.; Hoffman, Michael M.; Huisman, Merel; Isensee, Fabian; Jannin, Pierre; Kahn, Charles E.; Kainmueller, Dagmar; Kainz, Bernhard; Karargyris, Alexandros; Karthikesalingam, Alan; Kenngott, Hannes; Kleesiek, Jens; Kofler, Florian; Kooi, Thijs; Kopp-Schneider, Annette; Kozubek, Michal; Kreshuk, Anna; Kurc, Tahsin; Landman, Bennett A.; Litjens, Geert; Madani, Amin; Maier-Hein, Klaus; Martel, Anne L.; Mattson, Peter; Meijering, Erik; Menze, Bjoern; Moons, Karel G. M.; Müller, Henning; Nichyporuk, Brennan; Nickel, Felix; Petersen, Jens; Rafelski, Susanne M.; Rajpoot, Nasir; Reyes, Mauricio; Riegler, Michael A.; Rieke, Nicola; Saez-Rodriguez, Julio; Sánchez, Clara I.; Shetty, Shravya; Summers, Ronald M.; Taha, Abdel A.; Tiulpin, Aleksei; Tsaftaris, Sotirios A.; Van Calster, Ben; Varoquaux, Gaël; Yaniv, Ziv R.; Jäger, Paul F.; Maier-Hein, Lena; Pathology and Laboratory Medicine, School of Medicine
    Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.