Bibliografias temáticas / Large baseline image registration

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Literatura científica selecionada sobre o tema "Large baseline image registration"

Autor: Grafiati

Publicado: 12 de outubro de 2024

Última modificação: 31 de julho de 2025

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Artigos de revistas sobre o assunto "Large baseline image registration"

Fidler, A., B. Likar, F. Pernus, and U. Skaleric. "Impact of JPEG lossy image compression on quantitative digital subtraction radiography." Dentomaxillofacial Radiology 31, no. 2 (2002): 106–12. http://dx.doi.org/10.1038/sj/dmfr/4600670.

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OBJECTIVES The aim of the study was to evaluate the impact of JPEG lossy image compression on the estimation of alveolar bone gain by quantitative digital subtraction radiography (DSR). METHODS Nine dry domestic pig mandible posterior segments were radiographed three times ('Baseline', 'No change', and 'Gain') with standardized projection geometry. Bone gain was simulated by adding artificial bone chips (1, 4, and 15 mg). Images were either compressed before or after registration. No change areas in compressed and subtracted 'No change-Baseline' images and bone gain volumes in compressed and s

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Sun, Quan, Lei Liu, Zhaodong Niu, Yabo Li, Jingyi Zhang, and Zhuang Wang. "A Practical Star Image Registration Algorithm Using Radial Module and Rotation Angle Features." Remote Sensing 15, no. 21 (2023): 5146. http://dx.doi.org/10.3390/rs15215146.

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Star image registration is the most important step in the application of astronomical image differencing, stacking, and mosaicking, which requires high robustness, accuracy, and real-time capability on the part of the algorithm. At present, there are no high-performance registration algorithms available in this field. In the present paper, we propose a star image registration algorithm that relies only on radial module features (RMF) and rotation angle features (RAF) while providing excellent robustness, high accuracy, and good real-time performance. The test results on a large amount of simul

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Wang, Shuxin, Shilei Cao, Dong Wei, et al. "Alternative Baselines for Low-Shot 3D Medical Image Segmentation---An Atlas Perspective." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (2021): 634–42. http://dx.doi.org/10.1609/aaai.v35i1.16143.

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Low-shot (one/few-shot) segmentation has attracted increasing attention as it works well with limited annotation. State-of-the-art low-shot segmentation methods on natural images usually focus on implicit representation learning for each novel class, such as learning prototypes, deriving guidance features via masked average pooling, and segmenting using cosine similarity in feature space. We argue that low-shot segmentation on medical images should step further to explicitly learn dense correspondences between images to utilize the anatomical similarity. The core ideas are inspired by the clas

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Mo, Nan. "Brain image registration optimization method via SAM-Med3D multi-scale feature migration." BIO Web of Conferences 174 (2025): 03021. https://doi.org/10.1051/bioconf/202517403021.

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Aiming at the problems of insufficient anatomical structure constraints and limited feature expression ability in medical image registration, this paper proposes a registration optimization method based on SAM-Med3D and dynamic large kernel convolution. A fixed SAM-Med3D encoder was used to extract multi-scale anatomical prior (32×32×32 to 8×8×8 resolution), and a dynamic large kernel Convolution module (DLK) was used to capture long-range spatial dependencies. A cross-attention mechanism was designed to achieve hierarchical fusion of anatomical features and local details. Innovative introduct

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Strittmatter, Anika, Anna Caroli, and Frank G. Zöllner. "A Multistage Rigid-Affine-Deformable Network for Three-Dimensional Multimodal Medical Image Registration." Applied Sciences 13, no. 24 (2023): 13298. http://dx.doi.org/10.3390/app132413298.

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Multimodal image registration is an important component of medical image processing, allowing the integration of complementary information from various imaging modalities to improve clinical applications like diagnosis and treatment planning. We proposed a novel multistage neural network for three-dimensional multimodal medical image registration, which addresses the challenge of larger rigid deformations commonly present in medical images due to variations in patient positioning in different scanners and rigid anatomical structures. This multistage network combines rigid, affine and deformabl

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Yao, Guobiao, Jin Zhang, Jianya Gong, and Fengxiang Jin. "Automatic Production of Deep Learning Benchmark Dataset for Affine-Invariant Feature Matching." ISPRS International Journal of Geo-Information 12, no. 2 (2023): 33. http://dx.doi.org/10.3390/ijgi12020033.

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To promote the development of deep learning for feature matching, image registration, and three-dimensional reconstruction, we propose a method of constructing a deep learning benchmark dataset for affine-invariant feature matching. Existing images often have large viewpoint differences and areas with weak texture, which may cause difficulties for image matching, with respect to few matches, uneven distribution, and single matching texture. To solve this problem, we designed an algorithm for the automatic production of a benchmark dataset for affine-invariant feature matching. It combined two

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Schmit, Timothy J., Paul Griffith, Mathew M. Gunshor, Jaime M. Daniels, Steven J. Goodman, and William J. Lebair. "A Closer Look at the ABI on the GOES-R Series." Bulletin of the American Meteorological Society 98, no. 4 (2017): 681–98. http://dx.doi.org/10.1175/bams-d-15-00230.1.

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Abstract The Advanced Baseline Imager (ABI) on board the Geostationary Operational Environmental Satellite-R (GOES-R) is America’s next-generation geostationary advanced imager. GOES-R launched on 19 November 2016. The ABI is a state-of-the-art 16-band radiometer, with spectral bands covering the visible, near-infrared, and infrared portions of the electromagnetic spectrum. Many attributes of the ABI—such as spectral, spatial, and temporal resolution; radiometrics; and image navigation/registration—are much improved from the current series of GOES imagers. This paper highlights and discusses t

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Johnson, J. Patrick, Doniel Drazin, Wesley A. King, and Terrence T. Kim. "Image-guided navigation and video-assisted thoracoscopic spine surgery: the second generation." Neurosurgical Focus 36, no. 3 (2014): E8. http://dx.doi.org/10.3171/2014.1.focus13532.

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Object Video-assisted thoracoscopic surgery (VATS) has evolved for treatment of a variety of spinal disorders. Early incorporation with image-guided surgery (IGS) was challenged due to reproducibility and adaptability, limiting the procedure's acceptance. In the present study, the authors report their experience with second-generation IGS and VATS technologies for anterior thoracic minimally invasive spinal (MIS) procedures. Methods The surgical procedure is described in detail including operating room set-up, patient positioning (a lateral decubitus position), placement of the spinal referenc

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Wu, Zhenning, Xiaolei Lv, Ye Yun, and Wei Duan. "A Parallel Sequential SBAS Processing Framework Based on Hadoop Distributed Computing." Remote Sensing 16, no. 3 (2024): 466. http://dx.doi.org/10.3390/rs16030466.

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With the rapid development of microwave remote sensing and SAR satellite systems, the use of InSAR techniques has been greatly encouraged due to the abundance of SAR data with unprecedented temporal and spatial coverage. Small Baseline Subset (SBAS) is a promising time-series InSAR method for applications involving deformation monitoring of the Earth’s crust, and the sequential SBAS method is an extension of SBAS that allows long-term and large-scale surface displacements to be obtained with continuously auto-updating measurement results. As the Chinese LuTan-1 SAR system has begun acquiring m

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Li, Jingyi, Mengqi Han, Yongsen Chen, et al. "Prediction of recurrent ischaemic stroke using radiomics data and machine learning methods in patients with acute ischaemic stroke: protocol for a multicentre, large sample, prospective observational cohort study in China." BMJ Open 13, no. 10 (2023): e076406. http://dx.doi.org/10.1136/bmjopen-2023-076406.

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IntroductionStroke is a leading cause of mortality and disability worldwide. Recurrent strokes result in prolonged hospitalisation and worsened functional outcomes compared with the initial stroke. Thus, it is critical to identify patients who are at high risk of stroke recurrence. This study is positioned to develop and validate a prediction model using radiomics data and machine learning methods to identify the risk of stroke recurrence in patients with acute ischaemic stroke (AIS).Methods and analysisA total of 1957 patients with AIS will be needed. Enrolment at participating hospitals will

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Teses / dissertações sobre o assunto "Large baseline image registration"

Elassam, Abdelkarim. "Learning-based vanishing point detection and its application to large-baseline image registration." Electronic Thesis or Diss., Université de Lorraine, 2024. http://www.theses.fr/2024LORR0084.

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Cette thèse étudie la détection des points de fuite et de la ligne d'horizon ainsi que leur application à des tâches de localisation visuelle en environnement urbain. La localisation visuelle est un problème fondamental de vision par ordinateur qui vise à déterminer la position et l'orientation d'une caméra dans un environnement en se basant uniquement sur des informations visuelles. En environnements urbains et manufacturés, les points de fuite sont des repères visuels qui apportent des informations importantes sur la structure de la scène et leur détection est donc importante pour les tâches

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Al-Shahri, Mohammed. "Line Matching in a Wide-Baseline Stereoview." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1376951775.

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Lakemond, Ruan. "Multiple camera management using wide baseline matching." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/37668/1/Ruan_Lakemond_Thesis.pdf.

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Camera calibration information is required in order for multiple camera networks to deliver more than the sum of many single camera systems. Methods exist for manually calibrating cameras with high accuracy. Manually calibrating networks with many cameras is, however, time consuming, expensive and impractical for networks that undergo frequent change. For this reason, automatic calibration techniques have been vigorously researched in recent years. Fully automatic calibration methods depend on the ability to automatically find point correspondences between overlapping views. In typical camera

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Shao, Wei. "Identifying the shape collapse problem in large deformation image registration." Thesis, University of Iowa, 2016. https://ir.uiowa.edu/etd/2276.

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This thesis examines and identifies the problems of shape collapse in large deformation image registration. Shape collapse occurs in image registration when a region in the moving image is transformed into a set of near zero volume in the target image space. Shape collapse may occur when the moving image has a structure that is either missing or does not sufficiently overlap the corresponding structure in the target image. We state that shape collapse is a problem in image registration because it may lead to the following consequences: (1) Incorrect pointwise correspondence between different c

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Eiben, B. "Integration of biomechanical models into image registration in the presence of large deformations." Thesis, University College London (University of London), 2016. http://discovery.ucl.ac.uk/1476650/.

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Prone-to-supine breast image registration has potential application in the fields of surgical and radiotherapy planning, and image guided interventions. However, breast image registration of three-dimensional images acquired in different patient positions is a challenging problem, due to large deformations induced to the soft breast tissue caused by the change in gravity loading. Biomechanical modelling is a promising tool to predict gravity induced deformations, however such simulations alone are unlikely to produce good alignment due to inter-patient variability and image acquisition related

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Briand, Thibaud. "Image Formation from a Large Sequence of RAW Images : performance and accuracy." Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC1017/document.

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Le but de cette thèse est de construire une image couleur de haute qualité, contenant un faible niveau de bruit et d'aliasing, à partir d'une grande séquence (e.g. des centaines) d'images RAW prises avec un appareil photo grand public. C’est un problème complexe nécessitant d'effectuer à la volée du dématriçage, du débruitage et de la super-résolution. Les algorithmes existants produisent des images de haute qualité, mais le nombre d'images d'entrée est limité par des coûts de calcul et de mémoire importants. Dans cette thèse, nous proposons un algorithme de fusion d'images qui les traite séqu

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König, Lars [Verfasser]. "Matrix-free approaches for deformable image registration with large-scale and real-time applications in medical imaging / Lars König." Lübeck : Zentrale Hochschulbibliothek Lübeck, 2019. http://d-nb.info/1175137189/34.

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Matos, Ana Carolina Fonseca. "Development of a large baseline stereo vision rig for pedestrian and other target detection on road." Master's thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/17055.

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Mestrado em Engenharia Mecânica<br>Os veículos autónomos são uma tendência cada vez mais crescente nos dias de hoje com os grandes fabricantes da área automóvel, e não só, concentrados em desenvolver carros autónomos. As duas maiores vantagens que se destacam para os carros autónomos são maior conforto para o condutor e maior segurança, onde este trabalho se foca. São incontáveis as vezes que um condutor, por distração ou por outra razão, não vê um objeto na estrada e colide ou um peão na estrada que e atropelado. Esta e uma das questões que um sistema de apoio a condução (ADAS) ou

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Chnafa, Christophe. "Using image-based large-eddy simulations to investigate the intracardiac flow and its turbulent nature." Thesis, Montpellier 2, 2014. http://www.theses.fr/2014MON20112/document.

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Le premier objectif de cette thèse est de générer et d'analyser une base de données pour l'écoulement intracardiaque dans des géométries réalistes. Dans ce but, une stratégie couplant simulation numérique et imagerie médicale est appliquée à un cœur gauche pathologique et à un cœur gauche sain. Le second objectif est d'illustrer comment cette base de données peut être analysée afin de mieux comprendre l'écoulement intracardiaque, en portant une attention particulière aux caractéristiques instationnaires de l'écoulement et à sa nature turbulente. Une chaîne numérique pour simuler l'écoulement d

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Lotz, Johannes [Verfasser], Jan [Akademischer Betreuer] Modersitzki, and Heinz [Akademischer Betreuer] Handels. "Combined local and global image registration and its application to large-scale images in digital pathology / Johannes Lotz ; Akademische Betreuer: Jan Modersitzki, Heinz Handels." Lübeck : Zentrale Hochschulbibliothek Lübeck, 2020. http://d-nb.info/1217024069/34.

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Livros sobre o assunto "Large baseline image registration"

Schelbert, Heinrich R. Image-Based Measurements of Myocardial Blood Flow. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199392094.003.0024.

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Image-based measurements of myocardial blood flow afford the assessment of coronary circulatory function. They reflect functional consequences of coronary stenoses, diffuse epicardial vessel disease and microvascular dysfunction and structural changes and thus provide a measure of the total ischemic burden. Measured flows contain therefore clinically important predictive information. Fundamental to flow measurements are the tissue tracer kinetics, their description through tracer kinetic models, high spatial and temporal resolution imaging devices and accurate extraction of radiotracer tissue

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Capítulos de livros sobre o assunto "Large baseline image registration"

Risser, Laurent, François-Xavier Vialard, Maria Murgasova, Darryl Holm, and Daniel Rueckert. "Large Deformation Diffeomorphic Registration Using Fine and Coarse Strategies." In Biomedical Image Registration. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14366-3_17.

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Grothausmann, Roman, Dženan Zukić, Matt McCormick, Christian Mühlfeld, and Lars Knudsen. "Enabling Manual Intervention for Otherwise Automated Registration of Large Image Series." In Biomedical Image Registration. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50120-4_3.

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Delponte, Elisabetta, Francesco Isgrò, Francesca Odone, and Alessandro Verri. "Large Baseline Matching of Scale Invariant Features." In Image Analysis and Processing – ICIAP 2005. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11553595_97.

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He, Jianchun, and Gary E. Christensen. "Large Deformation Inverse Consistent Elastic Image Registration." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45087-0_37.

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Barrow, Michael, Nelson Ho, Alric Althoff, Peter Tueller, and Ryan Kastner. "Benchmarking Video with the Surgical Image Registration Generator (SIRGn) Baseline." In Advances in Visual Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33723-0_26.

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Zhang, Pei, Marc Niethammer, Dinggang Shen, and Pew-Thian Yap. "Large Deformation Diffeomorphic Registration of Diffusion-Weighted Images." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33418-4_22.

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Medan, Guy, Achia Kronman, and Leo Joskowicz. "Reduced-Dose Patient to Baseline CT Rigid Registration in 3D Radon Space." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10404-1_37.

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Mok, Tony C. W., and Albert C. S. Chung. "Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59716-0_21.

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Onofrey, John A., Lawrence H. Staib, and Xenophon Papademetris. "Semi-supervised Learning of Nonrigid Deformations for Image Registration." In Medical Computer Vision. Large Data in Medical Imaging. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05530-5_2.

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Trabalhos de conferências sobre o assunto "Large baseline image registration"

Zou, Haonan, Hong Zheng, Xin Jiang, Bin Jiang, and Qingling Xia. "LKGDIR: A Deformable Image Registration Network with Large Kernel Ghost Module." In 2024 8th Asian Conference on Artificial Intelligence Technology (ACAIT). IEEE, 2024. https://doi.org/10.1109/acait63902.2024.11021875.

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Li, Yinghao, Zhiyuan Dong, Mengyuan Yao, Yue Liu, and Yusong Lin. "A Pyramidal Feature Selection and Translation Approach for Large Deformation Image Registration." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821908.

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Wu, Jiong, and Kuang Gong. "Unsupervised Learning-Based Large Deformation Diffeomorphic Metric Mapping for Fast Image Registration." In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10981122.

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Li, Ruizhe, Grazziela Figueredo, Dorothee Auer, Christian Wagner, and Xin Chen. "MrRegNet: Multi-Resolution Mask Guided Convolutional Neural Network for Medical Image Registration with Large Deformations." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635510.

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Mahmood, Hassan, Farah Nawar, Syed Mohammed Shamsul Islam, and Asim Iqbal. "NeuroAtlas: An Artificial Intelligence-Based Framework for Annotation, Segmentation and Registration of Large Scale Biomedical Imaging Data." In 2024 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2024. https://doi.org/10.1109/dicta63115.2024.00055.

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Ramasamy, Manikandan, Mark Potsdam, and Gloria Yamauchi. "Measurements to Understand the Flow Mechanisms Contributing to Tandem-Rotor Outwash." In Vertical Flight Society 71st Annual Forum & Technology Display. The Vertical Flight Society, 2015. http://dx.doi.org/10.4050/f-0071-2015-10100.

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Downwash and outwash characteristics of a model-scale tandem-rotor system in the presence of the ground were analyzed by identifying and understanding the physical mechanisms contributing to the observed flow field behavior. A building block approach was followed in simplifying the problem, separating the effects of the fuselage, effects of one rotor on the other, etc. Flow field velocities were acquired in a vertical plane at four aircraft azimuths of a small-scale tandem rotor system using the particle image velocimetry (PIV) technique for radial distances up to 4 times the rotor diameter. R

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Caner, Gulcin, A. Murat Tekalp, Gaurav Sharma, and Wendi Heinzelman. "Multi-View Image Registration for Wide-Baseline Visual Sensor Networks." In 2006 International Conference on Image Processing. IEEE, 2006. http://dx.doi.org/10.1109/icip.2006.313170.

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"LOG-UNBIASED LARGE-DEFORMATION IMAGE REGISTRATION." In International Conference on Computer Vision Theory and Applications. SciTePress - Science and and Technology Publications, 2007. http://dx.doi.org/10.5220/0002048202720279.

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Jing Liu, Marian Chuang, Andrew Chisholm, and Pamela Cosman. "Image registration robust to sparse large errors." In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2015. http://dx.doi.org/10.1109/embc.2015.7318772.

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Hering, Alessa, and Stefan Heldmann. "Unsupervised learning for large motion thoracic CT follow-up registration." In Image Processing, edited by Elsa D. Angelini and Bennett A. Landman. SPIE, 2019. http://dx.doi.org/10.1117/12.2506962.

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Relatórios de organizações sobre o assunto "Large baseline image registration"

Blais-Stevens, A., A. Castagner, A. Grenier, and K D Brewer. Preliminary results from a subbottom profiling survey of Seton Lake, British Columbia. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/332277.

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Seton Lake is a freshwater fiord located in southwestern British Columbia, roughly 4 km west of Lillooet and 250 km north-northeast of Vancouver. Located in the Coast Mountains, it is an alpine lake about 22-km long and roughly 1-1.5 km wide. It is separated from nearby Anderson Lake, located to the west, by a large pre-historic rock avalanche deposit at Seton Portage. The lake stands at about 243 m above sea level and is up to about 150 m deep (BC gov., 1953). Water level is controlled by a hydroelectric dam (i.e., Seton dam) located at the eastern end of the lake. Here, the lake drains east

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