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Thematische Bibliographien / Image registration

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Auswahl der wissenschaftlichen Literatur zum Thema „Image registration“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 25. Juli 2025

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Zeitschriftenartikel zum Thema "Image registration"

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Alam, Fakhre, Sami UR Rahman, Nasser Tairan, Habib Shah, Mohammed Saeed Abohashrh, and Sohail Abbas. "An Automatic Medical Image Registration Approach Based on Common Sub-regions of Interest." Journal of Medical Imaging and Health Informatics 9, no. 2 (2019): 251–60. http://dx.doi.org/10.1166/jmihi.2019.2601.

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Accurate and efficient image registration, based on interested common sub-regions is still a challenging task in medical image analysis. This paper presents an automatic features based approach for the rigid and deformable registration of medical images using interested common sub-regions. In the proposed approach, interested common sub-regions in two images (target image and source image) are automatically detected and locally registered. The final global registration is performed, using the transformation parameters obtained from the local registration. Registration using interested common s

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Himthani, Naveen, Malte Brunn, Jae-Youn Kim, Miriam Schulte, Andreas Mang, and George Biros. "CLAIRE—Parallelized Diffeomorphic Image Registration for Large-Scale Biomedical Imaging Applications." Journal of Imaging 8, no. 9 (2022): 251. http://dx.doi.org/10.3390/jimaging8090251.

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We study the performance of CLAIRE—a diffeomorphic multi-node, multi-GPU image-registration algorithm and software—in large-scale biomedical imaging applications with billions of voxels. At such resolutions, most existing software packages for diffeomorphic image registration are prohibitively expensive. As a result, practitioners first significantly downsample the original images and then register them using existing tools. Our main contribution is an extensive analysis of the impact of downsampling on registration performance. We study this impact by comparing full-resolution registrations o

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Liang, Bo, Xi Chen, Lan Yu, et al. "High-precision Multichannel Solar Image Registration Using Image Intensity." Astrophysical Journal Supplement Series 261, no. 2 (2022): 10. http://dx.doi.org/10.3847/1538-4365/ac7232.

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Abstract Solar images observed in different channels with different instruments are crucial to the study of solar activity. However, the images have different fields of view, causing them to be misaligned. It is essential to accurately register the images for studying solar activity from multiple perspectives. Image registration is described as an optimizing problem from an image to be registered to a reference image. In this paper, we proposed a novel coarse-to-fine solar image registration method to register the multichannel solar images. In the coarse registration step, we used the regular

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Govindarajulu, S. "Image Registration on Satellite Images." IOSR Journal of Electronics and Communication Engineering 3, no. 5 (2012): 10–17. http://dx.doi.org/10.9790/2834-0351017.

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Roy, Abhisek, Pranab Kanti Roy, Anirban Mitra, Swarnali Daw, Dipannita Basu, and Sayan Chakraborty. "Revolutionizing nonrigid demons registration with the whale optimization algorithm." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 2372–80. https://doi.org/10.11591/ijece.v15i2.pp2372-2380.

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Image registration is one of the popular image transformation models in satellite and medical imaging currently. Image registration refers to image mapping with two or more than images. The ground-breaking fusion of the whale optimization algorithm and nonrigid demons registration (WOA- NDR) is applied in the current work to improve image registration's precision and effectiveness. NDR is an effective method for aligning images that have pliable structures. Nevertheless, it frequently runs into issues with local minima and massive deformations. To address these issues, WOA- which draws inspira

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Xu, Hong Kui, Ming Yan Jiang, and Ming Qiang Yang. "An Image Registration Method Combing Feature Constraint with Multilevel Strategy." Applied Mechanics and Materials 58-60 (June 2011): 286–91. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.286.

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A novel method combing feature constraint with multilevel strategy to improve simultaneously the registration accuracy and speed is proposed for non-parametric image registrations. To images between which the local difference is large, integrating feature constraint constructed with local structure information of images into objective function of image registration improves the registration accuracy. When applying feature constraint under multilevel strategy, parameter searching is prevented from entrapped into local extremum by using the optimization result on coarser levels as the starting p

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Roy, Abhisek, Pranab Kanti Roy, Anirban Mitra, Swarnali Daw, Dipannita Basu, and Sayan Chakraborty. "Revolutionizing nonrigid demons registration with the whale optimization algorithm." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 2372. https://doi.org/10.11591/ijece.v15i2.pp2372-2380.

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Image registration is one of the popular image transformation models in satellite and medical imaging currently. Image registration refers to image mapping with two or more than images. The ground-breaking fusion of the whale optimization algorithm and nonrigid demons registration (WOA-NDR) is applied in the current work to improve image registration's precision and effectiveness. NDR is an effective method for aligning images that have pliable structures. Nevertheless, it frequently runs into issues with local minima and massive deformations. To address these issues, WOA-which draws inspirati

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Pluim, J. P. W., and J. M. Fitzpatrick. "Image registration." IEEE Transactions on Medical Imaging 22, no. 11 (2003): 1341–43. http://dx.doi.org/10.1109/tmi.2003.819272.

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Zhang, Xiaorong, Siyuan Li, Zhongyang Xing, Binliang Hu, and Xi Zheng. "Automatic Registration of Remote Sensing High-Resolution Hyperspectral Images Based on Global and Local Features." Remote Sensing 17, no. 6 (2025): 1011. https://doi.org/10.3390/rs17061011.

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Automatic registration of remote sensing images is an important task, which requires the establishment of appropriate correspondence between the sensed image and the reference image. Nowadays, the trend of satellite remote sensing technology is shifting towards high-resolution hyperspectral imaging technology. Ever higher revisit cycles and image resolutions require higher accuracy and real-time performance for automatic registration. The push-broom payload is affected by the push-broom stability of the satellite platform and the elevation change of ground objects, and the obtained hyperspectr

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Watanabe, Yoichi, and Eunyoung Han. "Image registration accuracy of GammaPlan: a phantom study." Journal of Neurosurgery 109, Supplement (2008): 21–24. http://dx.doi.org/10.3171/jns/2008/109/12/s5.

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Object The authors evaluated the accuracy of the automatic image coregistration function implemented in the Leksell GammaPlan treatment planning software (Version 4C with MultiView Extension and Version 8.0). Methods The authors used a phantom with 9 landmarks (tips of thin cylindrical acrylic rods) evenly distributed in the treatment space. Two sets of images of the phantom were taken with both CT and MR imaging systems. The first image was obtained with the phantom aligned with the scanner's axis and the second scan was made by intentionally shifting and rotating the phantom relative to the

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Dissertationen zum Thema "Image registration"

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Prasai, Persis. "Multimodality image registration." Birmingham, Ala. : University of Alabama at Birmingham, 2006. http://www.mhsl.uab.edu/dt/2007m/prasai.pdf.

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Suri, Sahil. "Automatic image to image registration for multimodal remote sensing images." kostenfrei, 2010. https://mediatum2.ub.tum.de/node?id=967187.

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Wei, YaNing. "Image registration and matching." Thesis, University of Nottingham, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.430757.

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Habboush, Isam H. (Isam Hussein). "Image registration and fusion." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/37009.

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Yanovsky, Igor. "Unbiased nonlinear image registration." Diss., Restricted to subscribing institutions, 2008. http://proquest.umi.com/pqdweb?did=1619485511&sid=16&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Rohrer, Jonathan. "Accelerated nonrigid image registration." Berlin dissertation.de, 2009. http://d-nb.info/999883968/04.

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Bird, Joshua Campbell Cater. "Evaluation of Deformable Image Registration." Thesis, University of Canterbury. Physics, 2015. http://hdl.handle.net/10092/10577.

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Deformable image registration (DIR) is a type of registration that calculates a deformable vector field (DVF) between two image data sets and permits contour and dose propagation. However the calculation of a DVF is considered an ill-posed problem, as there is no exact solution to a deformation problem, therefore all DVFs calculated contain errors. As a result it is important to evaluate and assess the accuracy and limitations of any DIR algorithm intended for clinical use. The influence of image quality on the DIR algorithms performance was also evaluated. The hybrid DIR algorithm in RayStat

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Henson, Benjamin. "Image registration for sonar applications." Thesis, University of York, 2017. http://etheses.whiterose.ac.uk/19536/.

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This work develops techniques to estimate the motion of an underwater platform by processing data from an on-board sonar, such as a Forward Looking Sonar (FLS). Based on image registration, a universal algorithm has been developed and validated with in field datasets. The proposed algorithm gives a high quality registration to a fine (sub-pixel) precision using an adaptive filter and is suitable for both optical and acoustic images. The efficiency and quality of the result can be improved if an initial estimate of the motion is made. Therefore, a coarse (pixel-wide) registration algorithm is p

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Fitch, Alistair John. "Fast statistically robust image registration." Thesis, University of Surrey, 2003. http://epubs.surrey.ac.uk/844612/.

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Image registration is the automatic alignment of images. It is a fundamental task in computer vision. Image registration is challenging, in part, because of the wide range of applications with an equally wide range of content. Applications that require the automatic alignment of images include: super-resolution, face detection, video coding, medical imaging, mosaicking, post-production video effects, and satellite image registration. The wide and diverse range of applications have led to a wide and diverse range of image registration algorithms. An image registration algorithm is defined by it

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FEI, Baowei. "Image Registration for the Prostate." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1224274091.

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Bücher zum Thema "Image registration"

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Goshtasby, A. Ardeshir. Image Registration. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2458-0.

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Fischer, Bernd, Benoît M. Dawant, and Cristian Lorenz, eds. Biomedical Image Registration. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14366-3.

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Dawant, Benoît M., Gary E. Christensen, J. Michael Fitzpatrick, and Daniel Rueckert, eds. Biomedical Image Registration. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31340-0.

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Pluim, Josien P. W., Boštjan Likar, and Frans A. Gerritsen, eds. Biomedical Image Registration. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11784012.

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Špiclin, Žiga, Jamie McClelland, Jan Kybic, and Orcun Goksel, eds. Biomedical Image Registration. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50120-4.

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Klein, Stefan, Marius Staring, Stanley Durrleman, and Stefan Sommer, eds. Biomedical Image Registration. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92258-4.

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Gee, James C., J. B. Antoine Maintz, and Michael W. Vannier, eds. Biomedical Image Registration. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/b11804.

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Ourselin, Sébastien, and Marc Modat, eds. Biomedical Image Registration. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08554-8.

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Modat, Marc, Ivor Simpson, Žiga Špiclin, Wietske Bastiaansen, Alessa Hering, and Tony C. W. Mok, eds. Biomedical Image Registration. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-73480-9.

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Le Moigne, Jacqueline, Nathan S. Netanyahu, and Roger D. Eastman, eds. Image Registration for Remote Sensing. Cambridge University Press, 2009. http://dx.doi.org/10.1017/cbo9780511777684.

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Buchteile zum Thema "Image registration"

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Goshtasby, A. Ardeshir. "Image Descriptors." In Image Registration. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2458-0_5.

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Goshtasby, A. Ardeshir. "Introduction." In Image Registration. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2458-0_1.

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Goshtasby, A. Ardeshir. "Image Resampling and Compositing." In Image Registration. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2458-0_10.

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Goshtasby, A. Ardeshir. "Image Registration Methods." In Image Registration. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2458-0_11.

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Goshtasby, A. Ardeshir. "Similarity and Dissimilarity Measures." In Image Registration. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2458-0_2.

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Goshtasby, A. Ardeshir. "Point Detectors." In Image Registration. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2458-0_3.

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Goshtasby, A. Ardeshir. "Feature Extraction." In Image Registration. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2458-0_4.

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Goshtasby, A. Ardeshir. "Feature Selection and Heterogeneous Descriptors." In Image Registration. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2458-0_6.

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Goshtasby, A. Ardeshir. "Point Pattern Matching." In Image Registration. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2458-0_7.

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Goshtasby, A. Ardeshir. "Robust Parameter Estimation." In Image Registration. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2458-0_8.

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Konferenzberichte zum Thema "Image registration"

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Zhou, Lei, Nimu Yuan, Katjana Ehrlich, and Jinyi Qi. "NCF: neural correspondence field for medical image registration." In Image Processing, edited by Olivier Colliot and Jhimli Mitra. SPIE, 2025. https://doi.org/10.1117/12.3047329.

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Petrou, Maria, and George Lazaridis. "Image registration." In International Symposium on Remote Sensing, edited by Sebastiano B. Serpico. SPIE, 2003. http://dx.doi.org/10.1117/12.463523.

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Cho, Soojin, and Byunghyun Kim. "Image-driven Bridge Inspection Framework using Deep Learning and Image Registration." In IABSE Conference, Seoul 2020: Risk Intelligence of Infrastructures. International Association for Bridge and Structural Engineering (IABSE), 2020. http://dx.doi.org/10.2749/seoul.2020.269.

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<p>This paper proposes an image-driven bridge inspection framework using automated damage detection using deep learning technique and image registration. A state-of-the-art deep learning model, Cascade Mask R-CNN (Mask and Region-based Convolutional Neural Networks) is trained for detection of cracks, which is a representative damage type of bridges, from the images taken from a bridge. The model is trained with more than a thousand training images containing cracks as well as crack-like objects (hard negative samples). The images taken from a test bridge are input to a deep learning mod

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Jamil, Sehrish, and Gul E. Saman. "Image registration of medical images." In 2017 Intelligent Systems and Computer Vision (ISCV). IEEE, 2017. http://dx.doi.org/10.1109/isacv.2017.8054911.

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Chen, Zekang, Jia Wei, and Rui Li. "Unsupervised Multi-Modal Medical Image Registration via Discriminator-Free Image-to-Image Translation." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/117.

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In clinical practice, well-aligned multi-modal images, such as Magnetic Resonance (MR) and Computed Tomography (CT), together can provide complementary information for image-guided therapies. Multi-modal image registration is essential for the accurate alignment of these multi-modal images. However, it remains a very challenging task due to complicated and unknown spatial correspondence between different modalities. In this paper, we propose a novel translation-based unsupervised deformable image registration approach to convert the multi-modal registration problem to a mono-modal one. Specifi

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Tang, Chao, Xiaohui Xie, and Ruxu Du. "Improved Image Registration Technique Using Demons and B-Spline." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-65830.

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Non-rigid image registration is an important and challenge work in image processing. The demons algorithm is one of the most effective non-rigid image registration methods. However, it is only suitable for images with small deformation. In recent years, many improving techniques are proposed. The free form deformation method based on B-spline function is widely employed in non-rigid image registration and is good at dealing with large deformation image registration. However, the performance of the demons algorithm is better than that of the B-spline method in dealing with small deformation reg

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Rogelj, Peter, and Stanislav Kovacic. "Symmetric image registration." In Medical Imaging 2003, edited by Milan Sonka and J. Michael Fitzpatrick. SPIE, 2003. http://dx.doi.org/10.1117/12.480122.

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Jian-Kun Shen, B. J. Matuszewski, and Lik-Kwan Shark. "Deformable image registration." In rnational Conference on Image Processing. IEEE, 2005. http://dx.doi.org/10.1109/icip.2005.1530591.

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Kent, P. "Multiresolution image registration." In IEE Colloquium on `Multiresolution Modelling and Analysis in Image Processing and Computer Vision'. IEE, 1995. http://dx.doi.org/10.1049/ic:19950501.

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Shahhosseini, Saeid, Bahman Rezaie, and Vahid Emamian. "Sequential Image Registration for Astronomical Images." In 2012 IEEE International Symposium on Multimedia (ISM). IEEE, 2012. http://dx.doi.org/10.1109/ism.2012.65.

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Berichte der Organisationen zum Thema "Image registration"

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Parsons, G., J. Rafferty, and S. Zilles. Tag Image File Format (TIFF) - image/tiff MIME Sub-type Registration. RFC Editor, 1998. http://dx.doi.org/10.17487/rfc2302.

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Parsons, G., and J. Rafferty. Tag Image File Format (TIFF) - image/tiff MIME Sub-type Registration. RFC Editor, 2002. http://dx.doi.org/10.17487/rfc3302.

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Reed, R. A. Comparison of Subpixel Phase Correlation Methods for Image Registration. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada519383.

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Brower, K. L. Algorithm for image registration and clutter and jitter noise reduction. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/446383.

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McIntyre, L., G. Parsons, and J. Rafferty. Tag Image File Format Fax eXtended (TIFF-FX) - image/tiff-fx MIME Sub-type Registration. RFC Editor, 2002. http://dx.doi.org/10.17487/rfc3250.

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McIntyre, L., G. Parsons, and J. Rafferty. Tag Image File Format Fax eXtended (TIFF-FX) - image/tiff-fx MIME Sub-type Registration. RFC Editor, 2005. http://dx.doi.org/10.17487/rfc3950.

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Toet, Alexander. Registration of a Dynamic Multimodal Target Image Test Set for the Evaluation of Image Fusion Techniques. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada598370.

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Lundeen, T. F., A. K. Andrews, E. M. Perry, M. V. Whyatt, and K. L. Steinmaus. Development of automated image co-registration techniques: Part II - multisensor imagery. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/417000.

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Eichel, Paul. MREG V1.1 : a multi-scale image registration algorithm for SAR applications. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1095930.

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Zikan, Karel. An Efficient Exact Algorithm for the 'Least Squares Image Registration Problem. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada208725.

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