24,713 research outputs found

    Augmented reality application assessment for disseminating rock art

    Full text link
    [EN] Currently, marker-based tracking is the most used method to develop augmented reality (AR) applications (apps). However, this method cannot be applied in some complex and outdoor settings such as prehistoric rock art sites owing to the fact that the usage of markers is restricted on site. Thus, natural feature tracking methods have to be used. There is a wide range of libraries to develop AR apps based on natural feature tracking. In this paper, a comparative study of Vuforia and ARToolKit libraries is carried out, analysing factors such as distance, occlusion and lighting conditions that affect user experience in both indoor and outdoor environments, and eventually the app developer. Our analysis confirms that Vuforia¿s user experience indoor is better, faster and flicker-free whether the images are properly enhanced, but it does not work properly on site. Therefore, the development of AR apps for complex outdoor environments such as rock art sites should be performed with ARToolKit.The authors gratefully acknowledge the support from the Spanish Ministerio de Economia y Competitividad to the project HAR2014-59873-R. Similarly, the authors want to express their gratitude to the General Directorate of Culture and Heritage, Conselleria d'Educacio, Investigacio, Cultura i Esport, Generalitat Valenciana for letting us access and carry out research at the archaeological site.Blanco-Pons, S.; Carrión-Ruiz, B.; Lerma, JL. (2018). Augmented reality application assessment for disseminating rock art. Multimedia Tools and Applications. 78(8):10265-10286. https://doi.org/10.1007/s11042-018-6609-xS1026510286788Alahi A., Ortiz R., Vandergheynst P (2012) FREAK: fast retina keypoint. Comput Vis Pattern Recognit 510–517 . doi: https://doi.org/10.1109/CVPR.2012.6247715Amin D, Govilkar S (2015) Comparative study of augmented reality Sdk’S. Int J Comput Sci Appl 5:11–26. https://doi.org/10.1227/01.NEU.0000297044.82035.57ARCore ARCore - Google Developer | ARCore | Google Developers. https://developers.google.com/ar/ . Accessed 26 Jun 2018ARKit ARKit - Apple Developer. https://developer.apple.com/arkit/ . Accessed 26 Jun 2018ARToolkit (2017) ARToolkit. https://archive.artoolkit.org/ . Accessed 2 Oct 2017ARToolkit (2017) About. https://artoolkit.org/about-artoolkit . Accessed 11 Apr 2017ARToolkit (2017) Documentation. https://artoolkit.org/documentation/ . Accessed 12 Apr 2017ArUco ArUco: A minimal library for Augmented Reality applications based on OpenCV | Aplicaciones de la Visión Artificial. https://www.uco.es/investiga/grupos/ava/node/26 . Accessed 19 Apr 2018Azuma R (1997) A survey of augmented reality. Presence Teleoperators Virt Environ 6:355–385 . doi: 10.1.1.30.4999Azuma R, Baillot Y, Feiner S et al (2001) Recent advances in augmented reality. Ieee Comput Graph Appl 34–47. doi: https://doi.org/10.4061/2011/908468Blanco-Novoa O, Fernandez-Carames TM, Fraga-Lamas P, Vilar-Montesinos M (2018) A practical evaluation of commercial industrial augmented reality systems in an industry 4.0 shipyard. IEEE Access 6:1–1. https://doi.org/10.1109/ACCESS.2018.2802699Blanco-Pons S, Carrión-Ruiz B, Lerma JL (2016) Review of augmented reality and virtual reality techniques in rock art. Proc 8th Int Congress Archaeol Comput Graph Cult Herit Innov ‘ARQUEOLÓGICA 2.0L: 176–183Brancati N, Caggianese G, Frucci M et al (2017) Experiencing touchless interaction with augmented content on wearable head-mounted displays in cultural heritage applications. Pers Ubiquitous Comput 21:203–217. https://doi.org/10.1007/s00779-016-0987-8Cagalaban G, Kim S (2010) Multiple object tracking in unprepared environments using combined feature for augmented reality applications. Springer, Berlin, HeidelbergCamera-Calibration Camera Calibration App for Android [ARToolkit]. https://archive.artoolkit.org/documentation/doku.php?id=4_Android:android_camera_calibration . Accessed 16 Oct 2017Carmigniani J, Furht B, Anisetti M et al (2011) Augmented reality technologies, systems and applications. Multimed Tools Appl 51:341–377. https://doi.org/10.1007/s11042-010-0660-6Carrión-Ruiz B, Blanco-Pons S, Lerma JL (2016) Digital image analysis of the visible region through simulation of rock art paintings. Proc 8th Int Congress Archaeol Comput Graph, Cult Heritage Innov ‘ARQUEOLÓGICA 2.0.’: 169–175Chen CY, Chang BR, Sen HP (2014) Multimedia augmented reality information system for museum guidance. Pers Ubiquitous Comput 18:315–322. https://doi.org/10.1007/s00779-013-0647-1CRYENGINE CRYENGINE | The complete solution for next generation game development by Crytek. https://www.cryengine.com/ . Accessed 7 Jun 2017Domingo I, Carrión B, Blanco S, Lerma JL (2015) Evaluating conventional and advanced visible image enhancement solutions to produce digital tracings at el Carche rock art shelter. Digit Appl Archaeol Cult Herit 2:79–88. https://doi.org/10.1016/j.daach.2015.01.001Dos Santos AB, Dourado JB, Bezerra A (2016) ARToolkit and Qualcomm Vuforia: An Analytical Collation. Proc - 18th Symp Virt Augment Real SVR 2016:229–233. https://doi.org/10.1109/SVR.2016.46DroidAR (2017) DroidAR by bitstars. https://bitstars.github.io/droidar/ . Accessed 10 Dec 2017Engine U (2017) Unreal Engine. https://www.unrealengine.com/ . Accessed 10 Oct 2017Fiala M (2005) ARTag, a fiducial marker system using digital techniques. Proc IEEE Comput Soc Conf Comput Vis Pattern Recogn 2:590–596. https://doi.org/10.1109/CVPR.2005.74Fischer J, Eichler M, Bartz D, Straßer W (2007) A hybrid tracking method for surgical augmented reality. Comput Graph 31:39–52. https://doi.org/10.1016/j.cag.2006.09.007González C, Vallejo D, Albusac J, Castro J (2011) Realidad Aumentada. Un enfoque práctico con ARToolKit y Blender. 2–120Gutierrez JM, Molinero MA, Soto-Martín O, Medina CR (2015) Augmented reality technology spreads information about historical graffiti in temple of Debod. Procedia Comput Sci 75:390–397. https://doi.org/10.1016/j.procs.2015.12.262Haladová ZB, Szemzö R, Kovačovský T, Žižka J (2015) Utilizing Multispectral Scanning and Augmented Reality for Enhancement and Visualization of the Wooden Sculpture Restoration Process. Procedia Comput Sci 67:340–347. https://doi.org/10.1016/j.procs.2015.09.278Jamali SS, Shiratuddin MF, Wong KW, Oskam CL (2015) Utilising mobile-augmented reality for learning human anatomy. Procedia - Soc Behav Sci 197:659–668. https://doi.org/10.1016/j.sbspro.2015.07.054Khan D, Ullah S, Rabbi I (2015) Factors affecting the design and tracking of ARToolKit markers. Comput Stand Interf 41:56–66. https://doi.org/10.1016/j.csi.2015.02.006Khan D, Ullah S, Yan D et al (2018) Robust tracking through the design of high quality fiducial markers: an optimization tool for ARToolKit. IEEE Access 4:22421–22433. https://doi.org/10.1109/ACCESS.2018.2801028Kim SL, Suk HJ, Kang JH, et al (2014) Using unity 3D to facilitate mobile augmented reality game development. Internet things (WF-IoT), 2014 IEEE World Forum 21–26 . doi: https://doi.org/10.1109/WF-IoT.2014.6803110Kounavis CD, Kasimati AE, Zamani ED (2012) Enhancing the tourism experience through mobile augmented reality: challenges and prospects. Int J Eng Bus Manag 4:1–6. https://doi.org/10.5772/51644La Delfa GC, Monteleone S, Catania V et al (2016) Performance analysis of visualmarkers for indoor navigation systems. Front Inf Technol Electron Eng 17:730–740. https://doi.org/10.1631/FITEE.1500324Liu S, Ge S, Yu H (2016) Research on Robustness recognition algorithms in augmented reality. 3rd Int Conf Inf Sci Control Eng: 547–552. doi: https://doi.org/10.1109/ICISCE.2016.123Lowe DG (2004) Distinctive image features from scale invariant keypoints. Int J Comput Vis 60:91–11020042. https://doi.org/10.1023/B:VISI.0000029664.99615.94Lytridis C, Tsinakos A, Kazanidis I (2018) ARTutor—an augmented reality platform for interactive distance learning. Educ Sci 8:6. https://doi.org/10.3390/educsci8010006Marchand E, Uchiyama H, Spindler F et al (2016) Pose estimation for augmented reality : a hands-on survey. IEEE Trans Vis Comput Graph 22:2633–2651. https://doi.org/10.1109/TVCG.2015.2513408Martínez R, Villaverde V (2002) La cova dels cavalls en el Barranc de la ValltortaMarto AGR, Sousa AA, de Gonçalves A (2017) DinofelisAR demo augmented reality based on natural features. 12a Conferência Ibérica Sist e Tecnol Informação, Lisboa 64:852–861. https://doi.org/10.1016/j.procs.2015.08.638Moreels P, Perona P (2007) Evaluation of feature detectors and descriptors based on 3D objects. Int J Comput Vis 73:263–284. https://doi.org/10.1007/s11263-006-9967-1Pierdicca R, Frontoni E, Zingaretti P et al (2015) Making visible the invisible. augmented reality visualization for 3D reconstructions of archaeological sites. Augment Virt Real Sec Int Conf AVR 2015 9254:25–37. https://doi.org/10.1007/978-3-319-22888-4Rabbi I, Ullah S, Javed M, Zen K (2014) Analysis of ARToolKit fiducial markers attributes for robust tracking. 1st Int Conf Recent Trends Inf Commun Technol Anal 281–290Radkowski R, Oliver J (2013) Natural feature tracking augmented reality for on-site assembly assistance systems. In: Shumaker R (ed) Virtual, Augmented and Mixed Reality. Systems and Applications. VAMR 2013. Lecture Notes in Computer Science. Springer, Berlin, Heidelberg, pp 281–290Ridel B, Reuter P, Laviole J et al (2014) The revealing flashlight: interactive spatial augmented reality for detail exploration of cultural heritage artifacts. J Comput Cult Herit 7(6):1–6:18. https://doi.org/10.1145/2611376Seo J, Shim J, Choi JH, et al (2011) Enhancing marker-based AR technology. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 6773 LNCS:97–104 . doi: https://doi.org/10.1007/978-3-642-22021-0_12Seo J, Shim J, Choi JH et al (2011) Enhancing marker-based AR technology. In: International conference on virtual and mixed reality. virtual and mixed reality - new trends. Springer, Berlin, Heidelberg, pp 97–104Siltanen S (2015) Diminished reality for augmented reality interior design. Vis Comput 33:1–16. https://doi.org/10.1007/s00371-015-1174-zSörös G, Seichter H, Rautek P, Gröller E (2011) Augmented visualization with natural feature tracking. Proc 10th Int Conf Mob Ubiquitous Multimed 4–12. doi: https://doi.org/10.1145/2107596.2107597Uchiyama H, Marchand E (2012) Object detection and pose tracking for augmented reality: recent approaches. 18th Korea-Japan Jt Work Front Comput Vis 1–8Unity Unity. https://unity3d.com/es . Accessed 12 Oct 2017Vuforia (2017) Vuforia. https://www.vuforia.com/ . Accessed 2 Oct 2017Vuforia (2017) Vuforia-VuMark. https://library.vuforia.com/articles/Training/VuMark . Accessed 4 Apr 2017Vuforia (2017) Image targets. https://library.vuforia.com/articles/Training/Image-Target-Guide . Accessed 11 Apr 2017Wang H, Qin J, Zhang F (2015) A new interaction method for augmented reality based on ARToolKit. 2015 8th Int Congr Image Signal Process 578–583. doi: https://doi.org/10.1109/CISP.2015.7407945Wang G, Wang B, Zhong F et al (2015) Global optimal searching for textureless 3D object tracking. Vis Comput 31:979–988. https://doi.org/10.1007/s00371-015-1098-7Wu S, Oerlemans A, Bakker EM, Lew MS (2017) A comprehensive evaluation of local detectors and descriptors. Signal Process Image Commun 59:150–167. https://doi.org/10.1016/J.IMAGE.2017.06.010Xu Y, Wu Y, Zhou H, View M (2018) Multi-scale Voxel Hashing and Efficient 3D Representation for Mobile Augmented Reality. Cvpr 1618–1625 . doi: https://doi.org/10.1109/CVPRW.2018.0020

    Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

    Get PDF
    One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions

    Continuous maintenance and the future – Foundations and technological challenges

    Get PDF
    High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security

    Recent trends, technical concepts and components of computer-assisted orthopedic surgery systems: A comprehensive review

    Get PDF
    Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.Web of Science1923art. no. 519

    Planar Object Tracking in the Wild: A Benchmark

    Full text link
    Planar object tracking is an actively studied problem in vision-based robotic applications. While several benchmarks have been constructed for evaluating state-of-the-art algorithms, there is a lack of video sequences captured in the wild rather than in constrained laboratory environment. In this paper, we present a carefully designed planar object tracking benchmark containing 210 videos of 30 planar objects sampled in the natural environment. In particular, for each object, we shoot seven videos involving various challenging factors, namely scale change, rotation, perspective distortion, motion blur, occlusion, out-of-view, and unconstrained. The ground truth is carefully annotated semi-manually to ensure the quality. Moreover, eleven state-of-the-art algorithms are evaluated on the benchmark using two evaluation metrics, with detailed analysis provided for the evaluation results. We expect the proposed benchmark to benefit future studies on planar object tracking.Comment: Accepted by ICRA 201

    Co-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple Objects

    Get PDF
    In this paper we introduce Co-Fusion, a dense SLAM system that takes a live stream of RGB-D images as input and segments the scene into different objects (using either motion or semantic cues) while simultaneously tracking and reconstructing their 3D shape in real time. We use a multiple model fitting approach where each object can move independently from the background and still be effectively tracked and its shape fused over time using only the information from pixels associated with that object label. Previous attempts to deal with dynamic scenes have typically considered moving regions as outliers, and consequently do not model their shape or track their motion over time. In contrast, we enable the robot to maintain 3D models for each of the segmented objects and to improve them over time through fusion. As a result, our system can enable a robot to maintain a scene description at the object level which has the potential to allow interactions with its working environment; even in the case of dynamic scenes.Comment: International Conference on Robotics and Automation (ICRA) 2017, http://visual.cs.ucl.ac.uk/pubs/cofusion, https://github.com/martinruenz/co-fusio
    corecore