Relevant bibliographies by topics / 2D/2D registration

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  2. Dissertations / Theses
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Academic literature on the topic '2D/2D registration'

Author: Grafiati
Published: 3 June 2025
Last updated: 20 July 2025

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Journal articles on the topic "2D/2D registration"

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Zhang, Yue, Yue Wu, Wenping Ma, Maoguo Gong, Hao Li, and Biao Hou. "Partial Point Cloud Registration with Multi-view 2D Image Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 10 (2025): 10329–37. https://doi.org/10.1609/aaai.v39i10.33121.

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Learning representations from numerous 2D image data has shown promising performance, yet very few works apply this representations to point cloud registration. In this paper, we explore how to leverage the 2D information to assist the point cloud registration, and propose IAPReg, an Image-Assisted Partial 3D point cloud Registration framework with the multi-view images generated by the input point cloud. It is expected to enrich 3D information with 2D knowledge, and leverage 2D knowledge to assist with point cloud registration. Specifically, we create multi-view depth maps by projecting the i
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Sun, Haoran. "A Review of 3D-2D Registration Methods and Applications based on Medical Images." Highlights in Science, Engineering and Technology 35 (April 11, 2023): 200–224. http://dx.doi.org/10.54097/hset.v35i.7055.

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The registration of preoperative three-dimensional (3D) medical images with intraoperative two-dimensional (2D) data is a key technology for image-guided radiotherapy, minimally invasive surgery, and interventional procedures. In this paper, we review 3D-2D registration methods using computed tomography (CT) and magnetic resonance imaging (MRI) as preoperative 3D images and ultrasound, X-ray, and visible light images as intraoperative 2D images. The 3D-2D registration techniques are classified into intensity-based, structure-based, and gradient-based according to the different registration fea
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Edsfeldt, Andreas, Björn Sonesson, Helena Rosén, et al. "Validation of a New Method for 2D Fusion Imaging Registration in a System Prepared Only for 3D." Journal of Endovascular Therapy 27, no. 3 (2020): 468–72. http://dx.doi.org/10.1177/1526602820912223.

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Purpose: To validate a new 2D-3D registration method of fusion imaging during aortic repair in a system prepared only for 3D-3D registration and to compare radiation doses and accuracy. Materials and Methods: The study involved 189 patients, including 94 patients (median age 70 years; 85 men) who underwent abdominal endovascular aneurysm repair (EVAR) with 2D-3D fusion on an Artis zee imaging system and 95 EVAR patients (median age 70 years; 81 men) from a prior study who had 3D-3D registration done using cone beam computed tomography (CBCT). For the 2D-3D registration, an offline CBCT of the
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He, Yifeng, Jingui Zou, Ruoming Zhai, et al. "An Automatic Solution for Registration Between Single-Image and Point Cloud in Manhattan World Using Line Primitives." Remote Sensing 16, no. 23 (2024): 4382. http://dx.doi.org/10.3390/rs16234382.

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2D-3D registration is increasingly being applied in various scientific and engineering scenarios. However, due to appearance differences and cross-modal discrepancies, it is demanding for image and point cloud registration methods to establish correspondences, making 2D-3D registration highly challenging. To handle these problems, we propose a novel and automatic solution for 2D-3D registration in Manhattan world based on line primitives, which we denote as VPPnL. Firstly, we derive the rotation matrix candidates by establishing the vanishing point coordinate system as the link of point cloud
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Plattard, Delphine, Marine Soret, Jocelyne Troccaz, et al. "Patient Set-Up Using Portal Images: 2D/2D Image Registration Using Mutual Information." Computer Aided Surgery 5, no. 4 (2000): 246–62. http://dx.doi.org/10.3109/10929080009148893.

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Palaniswaamy, G., S. Goddu, M. Mamalui-Hunter, and D. Low. "SU-FF-J-74: Validation of Bony Anatomy Based (2D-2D) Image Registration." Medical Physics 36, no. 6Part6 (2009): 2493. http://dx.doi.org/10.1118/1.3181366.

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Gong, Ren Hui, Özgür Güler, Mustafa Kürklüoglu, John Lovejoy, and Ziv Yaniv. "Interactive initialization of 2D/3D rigid registration." Medical Physics 40, no. 12 (2013): 121911. http://dx.doi.org/10.1118/1.4830428.

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Lucas, Charles-Gérard, and Jérôme Gilles. "Demon Registration for 2D Empirical Wavelet Transforms." Foundations 4, no. 4 (2024): 690–703. https://doi.org/10.3390/foundations4040043.

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The empirical wavelet transform is a fully adaptive time-scale representation that has been widely used in the last decade. Inspired by the empirical mode decomposition, it consists of filter banks based on harmonic mode supports. Recently, it has been generalized to build the filter banks from any generating function using mappings. In practice, the harmonic mode supports can have a low-constrained shape in 2D, leading to numerical difficulties to estimate mappings adapted to the construction of empirical wavelet filters. This work aims to propose an efficient numerical scheme to compute empi
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Kubias, A., F. Deinzer, T. Feldmann, D. Paulus, B. Schreiber, and Th Brunner. "2D/3D image registration on the GPU." Pattern Recognition and Image Analysis 18, no. 3 (2008): 381–89. http://dx.doi.org/10.1134/s1054661808030048.

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Varnavas, Andreas, Tom Carrell, and Graeme Penney. "Fully automated 2D–3D registration and verification." Medical Image Analysis 26, no. 1 (2015): 108–19. http://dx.doi.org/10.1016/j.media.2015.08.005.

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Dissertations / Theses on the topic "2D/2D registration"

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Truong, Michael Vi Nguyen. "2D-3D registration of cardiac images." Thesis, King's College London (University of London), 2014. https://kclpure.kcl.ac.uk/portal/en/theses/2d3d-registration-of-cardiac-images(afef93e6-228c-4bc7-aab0-94f1e1ecf006).html.

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This thesis describes two novel catheter-based 2D-3D cardiac image registration algorithms for overlaying preoperative 3D MR or CT data onto intraoperative fluoroscopy, and fusing electroanatomical data onto clinical images. The work is intended for use in cardiac catheterisation procedures. To fulfil this objective, the algorithms must be accurate, robust and minimally disruptive to the clinical workflow. The first algorithm relies on the catheterisation of vessels of the heart and registers by minimising a vessel-radius-weighted distance between the catheters and corresponding vessel centrel
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Ingberg, Benjamin. "Registration of 2D Objects in 3D data." Thesis, Linköpings universitet, Datorseende, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119338.

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In the field of industrial automation large savings can be realized if position andorientation of an object is known. Knowledge about an objects position and orien-tation can be used by advanced robotic systems to be able to work with complexitems. Specifically 2D-objects are a big enough sub domain to motivate specialattention. Traditionally this problem has been solved with large mechanical sys-tems that forces the objects into specific configurations. Besides being expensive,taking up a lot of space and having great difficulty handling fragile items, thesemechanical systems have to be const
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Dorgham, Osama. "High speed 2D/3D medical image registration." Thesis, University of East Anglia, 2010. https://ueaeprints.uea.ac.uk/32227/.

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Pujol, Miró Alba. "Learning to extract features for 2D – 3D multimodal registration." Doctoral thesis, Universitat Politècnica de Catalunya, 2020. http://hdl.handle.net/10803/669664.

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The ability to capture depth information form an scene has greatly increased in the recent years. 3D sensors, traditionally high cost and low resolution sensors, are being democratized and 3D scans of indoor and outdoor scenes are becoming more and more common. However, there is still a great data gap between the amount of captures being performed with 2D and 3D sensors. Although the 3D sensors provide more information about the scene, 2D sensors are still more accessible and widely used. This trade-off between availability and information between sensors brings us to a multimodal scenario of
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Toledo, Acosta Bertha Mayela. "Multimodal image registration in 2D and 3D correlative microscopy." Thesis, Rennes 1, 2018. http://www.theses.fr/2018REN1S054/document.

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Cette thèse porte sur la définition d'un schéma de recalage automatique en microscopie corrélative 2D et 3D, en particulier pour des images de microscopie optique et électronique (CLEM). Au cours des dernières années, la CLEM est devenue un outil d'investigation important et puissant dans le domaine de la bio-imagerie. En utilisant la CLEM, des informations complémentaires peuvent être collectées à partir d'un échantillon biologique. La superposition des différentes images microscopiques est généralement réalisée à l'aide de techniques impliquant une assistance manuelle à plusieurs étapes, ce
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Hössjer, Simon. "3D/2D Image Registration for Patient Positioning in Stereotactic Radiosurgery." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-170639.

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In the application of stereotactic radiosurgery for treatment of brain tumors it is imperative that patients are positioned with sub-millimetre accuracy so as to not damage surrounding healthy tissue during treatment. We investigate how feasible the technique of digitally reconstructed radiographs is as a registration method for patient positioning in this type of high-accuracy application. In particular, since most registration methods based on said technique only rely on two simultaneous projection angles seldom reaching high enough accuracy, we consider any arbitrary amount and observe its
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Dowell, Rachel J. (Rachel Jean). "Registration of 2D ultrasound images in preparation for 3D reconstruction." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/10181.

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Poulain, Emmanuelle. "Recalage déformable entre angioscanner cardiaque 3D statique et angiographie coronaire dynamique 2D+t." Thesis, Université Côte d'Azur (ComUE), 2019. http://www.theses.fr/2019AZUR4068/document.

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L’angioplastie coronarienne est une intervention guidée par rayons X et réalisée par voie endovasculaire qui restaure le diamètre des vaisseaux coronaires réduit par accumulation de corps gras au sein de leurs parois. Lors de cette procédure, un guide est introduit à partir d’une artère qui peut être située au poignet ou au niveau de l’aine. La technique consiste alors à amener dans le vaisseau pathologique un ballonnet gonflable dans la zone rétrécie, localisée grâce à l’injection préalable d’un produit de contraste. Le gonflement du ballonnet élargit l’artère et s’accompagne en général de la
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Zhang, Yan. "Feature-based automatic registration of images with 2D and 3D models." Thesis, University of Central Lancashire, 2006. http://clok.uclan.ac.uk/21603/.

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Automatic image registration is the technique to align images in different coordinate systems to the same coordinate system which has found wide industrial applications for control automation and quality inspection. Focusing on the industrial applications where product models are available and transformations between models and images are global, this thesis presents the research works on two registration problems based on different features and different transformation models. The first image registration problem is a 2D/2D one with a 2D similarity transformation and based on geometric primit
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Alhrishy, Mazen. "Interventional vascular enhancement using digital tomosynthesis facilitated by 2D-3D registration." Thesis, King's College London (University of London), 2016. https://kclpure.kcl.ac.uk/portal/en/theses/interventional-vascular-enhancement-using-digital-tomosynthesis-facilitated-by-2d3d-registration(3251a548-fd1d-4f7c-ad1e-d31ce13c40d3).html.

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Endovascular image-guided surgery (EIGS) using C-arm fluoroscopy is being per- formed for more and more complex procedures with longer imaging times. An example of such complex procedures is fenestrated endovascular aneurysm repair (EVAR), which involves delivering bespoke stent-grafts through vasculature into the aneurismal area using low dose images. However, X-ray is much better at visualizing interventional devices and dense structures compared to vasculature. To enhance vascular visualisation, iodinated contrast medium (ICM) is injected and a significantly higher X-ray dose than standard
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Book chapters on the topic "2D/2D registration"

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Müller, Ulrich, Jürgen Hesser, and Reinhard Männer. "Fast Rigid 2D-2D Multimodal Registration." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30135-6_108.

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Jaganathan, Srikrishna, Jian Wang, Anja Borsdorf, Karthik Shetty, and Andreas Maier. "Deep Iterative 2D/3D Registration." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87202-1_37.

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Lange, Annkristin, and Stefan Heldmann. "Multilevel 2D-3D Intensity-Based Image Registration." In Biomedical Image Registration. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50120-4_6.

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Vansteenkiste, Ewout, Jef Vandemeulebroucke, and Wilfried Philips. "2D/3D Registration of Neonatal Brain Images." In Biomedical Image Registration. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11784012_33.

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Bakic, Predrag R., Frederic J. P. Richard, and Andrew D. A. Maidment. "2D-to-3D X-Ray Breast Image Registration." In Biomedical Image Registration. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11784012_11.

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Tomaževič, Dejan, Boštjan Likar, and Franjo Pernuš. "Reconstruction-Based 3D/2D Image Registration." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11566489_29.

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van de Kraats, Everine B., Graeme P. Penney, Dejan Tomaževič, Theo van Walsum, and Wiro J. Niessen. "Standardized Evaluation of 2D-3D Registration." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30135-6_70.

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Klingenbeck, Klaus. "2D-3D Registration for Interventional Procedures: A Clinical Perspective." In Biomedical Image Registration. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39701-4_22.

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Thivierge-Gaulin, David, Chen-Rui Chou, Atilla P. Kiraly, Christophe Chefd’Hotel, Norbert Strobel, and Farida Cheriet. "3D-2D Registration Based on Mesh-Derived Image Bisection." In Biomedical Image Registration. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31340-0_8.

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Dubra, Alfredo, and Zachary Harvey. "Registration of 2D Images from Fast Scanning Ophthalmic Instruments." In Biomedical Image Registration. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14366-3_6.

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Conference papers on the topic "2D/2D registration"

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Zhou, Duo, Shibin Yin, and Hongbo Xie. "An improved ICP registration algorithm for 2D point cloud." In 2024 6th International Conference on Intelligent Control, Measurement and Signal Processing (ICMSP). IEEE, 2024. https://doi.org/10.1109/icmsp64464.2024.10866567.

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Gopalakrishnan, Vivek, Neel Dey, and Polina Golland. "Intraoperative 2D/3D Image Registration via Differentiable X-Ray Rendering." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.01108.

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Arya, K. V. "An efficient 2D image registration algorithm." In 2009 International Conference on Industrial and Information Systems (ICIIS). IEEE, 2009. http://dx.doi.org/10.1109/iciinfs.2009.5429838.

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Huhle, Benjamin, Timo Schairer, Andreas Schilling, and Wolfgang Straßer. "6DoF Registration of 2D Laser Scans." In 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT). IEEE, 2011. http://dx.doi.org/10.1109/3dimpvt.2011.26.

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Ma, Burton, James Stewart, David Pichora, Randy Ellis, and Purang Abolmaesumi. "2D/3D Registration of Multiple Bones." In 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2007. http://dx.doi.org/10.1109/iembs.2007.4352426.

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Duraisamy, Prakash, Yassine Belkhouche, Stephen Jackson, Kamesh Namuduri, and Bill Buckles. "2D-3D registration using intensity gradients." In SPIE Optical Engineering + Applications, edited by Oliver E. Drummond. SPIE, 2011. http://dx.doi.org/10.1117/12.894288.

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Mitrović, Uroš, Žiga Špiclin, Boštjan Likar, and Franjo Pernuš. "Evaluation of 3D-2D registration methods for registration of 3D-DSA and 2D-DSA cerebral images." In SPIE Medical Imaging, edited by Sebastien Ourselin and David R. Haynor. SPIE, 2013. http://dx.doi.org/10.1117/12.2007009.

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Paudel, Danda Pani, Cedric Demonceaux, Adlane Habed, and Pascal Vasseur. "Localization of 2D Cameras in a Known Environment Using Direct 2D-3D Registration." In 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014. http://dx.doi.org/10.1109/icpr.2014.43.

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ceccarelli, Michele, and Jens Michael Carstensen. "Bayesian Grid matching for 2D gel registration." In 2010 IEEE International Conference on Imaging Systems and Techniques (IST). IEEE, 2010. http://dx.doi.org/10.1109/ist.2010.5548506.

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Gong, Ren Hui, Purang Abolmaesumi, and James Stewart. "A Robust Technique for 2D-3D Registration." In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.259227.

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