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Articoli di riviste sul tema "Image registration"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 25 luglio 2025

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1

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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5

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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6

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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7

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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Abstract (sommario):

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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8

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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10

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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11

Watcharawipha, Anirut, Nipon Theera-Umpon, and Sansanee Auephanwiriyakul. "Space Independent Image Registration Using Curve-Based Method with Combination of Multiple Deformable Vector Fields." Symmetry 11, no. 10 (2019): 1210. http://dx.doi.org/10.3390/sym11101210.

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This paper proposes a novel curve-based or edge-based image registration technique that utilizes the curve transformation function and Gaussian function. It enables deformable image registration between images in different spaces, e.g., different color spaces or different medical image modalities. In particular, piecewise polynomial fitting is used to fit a curve and convert it to the global cubic B-spline control points. The transformation between the curves in the reference and source images are performed by using these control points. The image area is segmented with respect to the referenc

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12

Bingjian, Wang, Lu Quan, Li Yapeng, et al. "Image registration method for multimodal images." Applied Optics 50, no. 13 (2011): 1861. http://dx.doi.org/10.1364/ao.50.001861.

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Nobnop, Wannapha, Imjai Chitapanarux, Somsak Wanwilairat, Ekkasit Tharavichitkul, Vicharn Lorvidhaya, and Patumrat Sripan. "Effect of Deformation Methods on the Accuracy of Deformable Image Registration From Kilovoltage CT to Tomotherapy Megavoltage CT." Technology in Cancer Research & Treatment 18 (January 1, 2019): 153303381882118. http://dx.doi.org/10.1177/1533033818821186.

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Introduction: The registration accuracy of megavoltage computed tomography images is limited by low image contrast when compared to that of kilovoltage computed tomography images. Such issues may degrade the deformable image registration accuracy. This study evaluates the deformable image registration from kilovoltage to megavoltage images when using different deformation methods and assessing nasopharyngeal carcinoma patient images. Methods: The kilovoltage and the megavoltage images from the first day and the 20th fractions of the treatment day of 12 patients with nasopharyngeal carcinoma we

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14

Ademaj, Adela, Lavdie Rada, Mazlinda Ibrahim, and Ke Chen. "A variational joint segmentation and registration framework for multimodal images." Journal of Algorithms & Computational Technology 14 (January 2020): 174830262096669. http://dx.doi.org/10.1177/1748302620966691.

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Image segmentation and registration are closely related image processing techniques and often required as simultaneous tasks. In this work, we introduce an optimization-based approach to a joint registration and segmentation model for multimodal images deformation. The model combines an active contour variational term with mutual information (MI) smoothing fitting term and solves in this way the difficulties of simultaneously performed segmentation and registration models for multimodal images. This combination takes into account the image structure boundaries and the movement of the objects,

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15

Zhang, Xuming, Yao Zhou, Peng Qiao, et al. "Image Registration Algorithm for Remote Sensing Images Based on Pixel Location Information." Remote Sensing 15, no. 2 (2023): 436. http://dx.doi.org/10.3390/rs15020436.

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Registration between remote sensing images has been a research focus in the field of remote sensing image processing. Most of the existing image registration algorithms applied to feature point matching are derived from image feature extraction methods, such as scale-invariant feature transform (SIFT), speed-up robust features (SURF) and Siamese neural network. Such methods encounter difficulties in achieving accurate image registration where there is a large bias in the image features or no significant feature points. Aiming to solve this problem, this paper proposes an algorithm for multi-so

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YAKOVENKO, D. S., and O. N. KANEVA. "MULTIMODAL IMAGE REGISTRATION ALGORITHM." Applied Mathematics and Fundamental Informatics 10, no. 4 (2023): 57–63. https://doi.org/10.25206/2311-4908-2023-10-4-57-63.

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An algorithm for spatial image registration based on the analysis of the geometric location of the points is proposed. It allows you to search for a similarity transformation matrix between two images, including from different modalities. Calculations are based on the comparison of points (labels) previously marked on the processed images. Labels may not be ordered relative to each other and marked up by both a person and another algorithm. The algorithm can be applied in cases with very different registered images.

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Sivaramakrishna, Radhika. "3D Breast Image Registration — A Review." Technology in Cancer Research & Treatment 4, no. 1 (2005): 39–48. http://dx.doi.org/10.1177/153303460500400106.

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Image registration is an important problem in breast imaging. It is used in a wide variety of applications that include better visualization of lesions on pre- and post-contrast breast MRI images, speckle tracking and image compounding in breast ultrasound images, alignment of positron emission, and standard mammography images on hybrid machines et cetera. It is a prerequisite to align images taken at different times to isolate small interval lesions. Image registration also has useful applications in monitoring cancer therapy. The field of breast image registration has gained considerable int

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Jiao, Jichao, Wenyi Li, Zhongliang Deng, and Qasim Ali Arain. "A structural similarity-inspired performance assessment model for multisensor image registration algorithms." International Journal of Advanced Robotic Systems 14, no. 4 (2017): 172988141771705. http://dx.doi.org/10.1177/1729881417717059.

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In order to assess the performance of multisensor image registration algorithms that are used in the multirobot information fusion, we propose a model based on structural similarity whose name is vision registration assessment model. First of all, this article introduces a new image concept named superimposed image for testing subjective and objective assessment methods. Therefore, we assess the superimposed image but not the registered image, which is different from previous image registration assessment methods that usually use reference and sensed images. Then, we calculate eight assessment

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19

Tang, Jun. "Image Registration Using Clustering Algorithm." Advanced Materials Research 108-111 (May 2010): 63–68. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.63.

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This paper proposed a new method of image registration based on clustering algorithm. It used clustering algorithm to cluster all the feature vectors of images, and adopted EM algorithm to optimize the parameters and algorithm. Experimental result shows that the proposed image registration method can improve the precise of image registration, and reduce error.

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20

Pan, Meisen, Jianjun Jiang, Fen Zhang, and Qiusheng Rong. "MEDICAL IMAGE REGISTRATION BASED ON IMPROVED FUZZY C-MEANS CLUSTERING." Biomedical Engineering: Applications, Basis and Communications 27, no. 04 (2015): 1550032. http://dx.doi.org/10.4015/s1016237215500325.

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The mutual information (MI) technology and the iterative closest point (ICP) algorithm, as intensity-based and feature-based image registration methods respectively, are commonly put into use in medical image registration. But some naturally existing things which restrict the further development need to be faced and be solved. On one hand, they remain heavy calculation costs and low registration efficiencies. On the other hand, since they seriously depend on whether the initial rotation and translation registration parameters can be exactly selected, they often trap in the local optima and eve

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21

Yang, Lu Jing, Wei Hao, and Chong Lun Li. "A Modified Phase Correlation Method for Image Registration." Applied Mechanics and Materials 48-49 (February 2011): 48–51. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.48.

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Image registration is a very fundamental and important part in many multi-sensor image based applications. Phase correlation-based image registration method is widely concerned for its small computation amount, strong anti-interference property. However, it can only solve the image registration problem with translational motion. Hence, we proposed a modified phase correlation registration method in the paper. We analyzed the principle of registration, gave the flow chart, and applied the method to the SAR image registration problems with scaling, rotation and translation transformation. Simula

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22

Rodgers, John, Rosie Hales, Lee Whiteside, et al. "Comparison of radiographer interobserver image registration variability using cone beam CT and MR for cervix radiotherapy." British Journal of Radiology 93, no. 1112 (2020): 20200169. http://dx.doi.org/10.1259/bjr.20200169.

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Objectives: The aim of this study was to assess the consistency of therapy radiographers performing image registration using cone beam computed tomography (CBCT)-CT, magnetic resonance (MR)-CT, and MR-MR image guidance for cervix cancer radiotherapy and to assess that MR-based image guidance is not inferior to CBCT standard practice. Methods: 10 patients receiving cervix radiation therapy underwent daily CBCT guidance and magnetic resonance (MR) imaging weekly during treatment. Offline registration of each MR image, and corresponding CBCT, to planning CT was performed by five radiographers. MR

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23

Zhu, Ning, Mohammad Najafi, Bin Han, Steven Hancock, and Dimitre Hristov. "Feasibility of Image Registration for Ultrasound-Guided Prostate Radiotherapy Based on Similarity Measurement by a Convolutional Neural Network." Technology in Cancer Research & Treatment 18 (January 1, 2019): 153303381882196. http://dx.doi.org/10.1177/1533033818821964.

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Purpose: Registration of 3-dimensional ultrasound images poses a challenge for ultrasound-guided radiation therapy of the prostate since ultrasound image content changes significantly with anatomic motion and ultrasound probe position. The purpose of this work is to investigate the feasibility of using a pretrained deep convolutional neural network for similarity measurement in image registration of 3-dimensional transperineal ultrasound prostate images. Methods: We propose convolutional neural network-based registration that maximizes a similarity score between 2 identical in size 3-dimension

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A. Yorke, Afua, Gary C. McDonald, David Solis, and Thomas Guerrero. "Quality Assurance of Image Registration Using Combinatorial Rigid Registration Optimization (CORRO)." Cancer Research and Cellular Therapeutics 5, no. 3 (2021): 01–09. http://dx.doi.org/10.31579/2640-1053/076.

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Purpose: Expert selected landmark points on clinical image pairs to provide a basis for rigid registration validation. Using combinatorial rigid registration optimization (CORRO) provide a statistically characterized reference data set for image registration of the pelvis by estimating optimal registration. Materials ad Methods: Landmarks for each CT/CBCT image pair for 58 cases were identified. From the landmark pairs, combination subsets of k-number of landmark pairs were generated without repeat, forming k-set for k=4, 8, and 12. A rigid registration between the image pairs was computed for

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Chen, Bowei, Li Chen, Umara Khalid, and Shuai Zhang. "IFSrNet: Multi-Scale IFS Feature-Guided Registration Network Using Multispectral Image-to-Image Translation." Electronics 13, no. 12 (2024): 2240. http://dx.doi.org/10.3390/electronics13122240.

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Multispectral image registration is the process of aligning the spatial regions of two images with different distributions. One of the main challenges it faces is to resolve the severe inconsistencies between the reference and target images. This paper presents a novel multispectral image registration network, Multi-scale Intuitionistic Fuzzy Set Feature-guided Registration Network (IFSrNet), to address multispectral image registration. IFSrNet generates pseudo-infrared images from visible images using Cycle Generative Adversarial Network (CycleGAN), which is equipped with a multi-head attenti

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Jiang, Ping, Sijia Wu, Wenjian Qin, and Yaoqin Xie. "Complex Large-Deformation Multimodality Image Registration Network for Image-Guided Radiotherapy of Cervical Cancer." Bioengineering 11, no. 12 (2024): 1304. https://doi.org/10.3390/bioengineering11121304.

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In recent years, image-guided brachytherapy for cervical cancer has become an important treatment method for patients with locally advanced cervical cancer, and multi-modality image registration technology is a key step in this system. However, due to the patient’s own movement and other factors, the deformation between the different modalities of images is discontinuous, which brings great difficulties to the registration of pelvic computed tomography (CT/) and magnetic resonance (MR) images. In this paper, we propose a multimodality image registration network based on multistage transformati

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Zhou, Wu, and Yaoqin Xie. "Interactive Multigrid Refinement for Deformable Image Registration." BioMed Research International 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/532936.

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Deformable image registration is the spatial mapping of corresponding locations between images and can be used for important applications in radiotherapy. Although numerous methods have attempted to register deformable medical images automatically, such as salient-feature-based registration (SFBR), free-form deformation (FFD), and demons, no automatic method for registration is perfect, and no generic automatic algorithm has shown to work properly for clinical applications due to the fact that the deformation field is often complex and cannot be estimated well by current automatic deformable r

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Christensen, G. E., and H. J. Johnson. "Consistent image registration." IEEE Transactions on Medical Imaging 20, no. 7 (2001): 568–82. http://dx.doi.org/10.1109/42.932742.

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Bondzulic, Boban. "Image registration introduction." Vojnotehnicki glasnik 57, no. 3 (2009): 88–110. http://dx.doi.org/10.5937/vojtehg0903088b.

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Pernuš, Franjo, H. Siegfried Srtiehl, and Max A. Viergever. "Biomedical Image Registration." Image and Vision Computing 19, no. 1-2 (2001): 1–2. http://dx.doi.org/10.1016/s0262-8856(00)00071-8.

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Hill, Derek L. G., Philipp G. Batchelor, Mark Holden, and David J. Hawkes. "Medical image registration." Physics in Medicine and Biology 46, no. 3 (2001): R1—R45. http://dx.doi.org/10.1088/0031-9155/46/3/201.

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Chen, Chin-Tu. "Radiologic Image Registration." Academic Radiology 10, no. 3 (2003): 239–41. http://dx.doi.org/10.1016/s1076-6332(03)80096-x.

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Weber, D. A., and M. Ivanovic. "Correlative image registration." Seminars in Nuclear Medicine 24, no. 4 (1994): 311–23. http://dx.doi.org/10.1016/s0001-2998(05)80021-2.

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Rogelj, Peter, and Stanislav Kovačič. "Symmetric image registration." Medical Image Analysis 10, no. 3 (2006): 484–93. http://dx.doi.org/10.1016/j.media.2005.03.003.

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Kneöaurek, Karin, Marija Ivanovic, Josef Machac, and David A. Weber. "Medical image registration." Europhysics News 31, no. 4 (2000): 5–8. http://dx.doi.org/10.1051/epn:2000401.

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McCool, D., K. L. Adamson, J. R. Buscombe, and A. J. W. Hilson. "14. Image registration." Nuclear Medicine Communications 18, no. 4 (1997): 326. http://dx.doi.org/10.1097/00006231-199704000-00128.

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Soh, Youngsung, Mudasar Qadir, Aamer Mehmood, Yongsuk Hae, Hadi Ashraf, and Intaek Kim. "A Featured Area-Based Image Registration." International Journal of Computer Theory and Engineering 6, no. 5 (2014): 407–11. http://dx.doi.org/10.7763/ijcte.2014.v6.899.

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Huang, Min, Guanyu Ren, Shizheng Zhang, Qian Zheng, and Huiyang Niu. "An Unsupervised 3D Image Registration Network for Brain MRI Deformable Registration." Computational and Mathematical Methods in Medicine 2022 (October 3, 2022): 1–10. http://dx.doi.org/10.1155/2022/9246378.

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In recent years, deep learning has made successful applications and remarkable achievements in the field of medical image registration, and the method of medical image registration based on deep learning has become the current research hotspot. However, the performance of convolutional neural networks may not be fully exploited due to neglect of spatial relationships between distant locations in the image and incomplete updates of network parameters. To avoid this phenomenon, MHNet, a multiscale hierarchical deformable registration network for 3D brain MR images, was proposed in this paper. Th

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Wei, Chun Rong, Chu He, and Hong Sun. "SAR Image Registration Using Ratio Mutual Information." Applied Mechanics and Materials 241-244 (December 2012): 2630–37. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.2630.

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In order to reduce the noise sensitivity of the SAR (synthetic aperture radar) image registration, a image registration algorithm which basing on the ratio mutual information (RatioMI) is proposed in this paper. Firstly, the ratio images of the reference image and the floating image are gotten by using the ratio operator, and then take the two ratio images as a similar characteristic quantity to construct the similarity measure function which was used in the optimization process of the image registration experiment. The experimental results of the SAR image registration show that the new regis

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Salasiah Mokri, Siti, Ashrani Abd Rahni, Seri Mastura Mustaza, Norazreen Abd Aziz, Noraishikin Zulkarnain, and Nasharuddin Zainal. "Registration of Thoracic CT-CT Images Using Improved Demon Registration." International Journal of Engineering & Technology 8, no. 1.2 (2019): 226–34. http://dx.doi.org/10.14419/ijet.v8i1.2.24907.

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Computed Tomography (CT) image is commonly used for medical diagnosis, to monitor disease progression as well in radiotherapy planning and treatment. In basis, image registration aims to accurately align two or more monomodal or multimodal images taken at different time or places. In order to accurately register two CT images, an accurate and reliable registration algorithm is required. This paper proposes an improved Demon registration technique that uses sum of conditional variance (SCV) and multi-modality independent neighborhood descriptive (MIND) similarity metrics instead of the conventi

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Yang, Aolin, Tiejun Yang, Xiang Zhao, Xin Zhang, Yanghui Yan, and Chunxia Jiao. "DTR-GAN: An Unsupervised Bidirectional Translation Generative Adversarial Network for MRI-CT Registration." Applied Sciences 14, no. 1 (2023): 95. http://dx.doi.org/10.3390/app14010095.

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Medical image registration is a fundamental and indispensable element in medical image analysis, which can establish spatial consistency among corresponding anatomical structures across various medical images. Since images with different modalities exhibit different features, it remains a challenge to find their exact correspondence. Most of the current methods based on image-to-image translation cannot fully leverage the available information, which will affect the subsequent registration performance. To solve the problem, we develop an unsupervised multimodal image registration method named

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Huang, Xingyu, Jian Zhang, Kun Tang, Xinyu Cheng, Chen Ye, and Lihui Wang. "Multilevel network for large deformation image registration based on feature consistency and flow normalization." Medical Physics 51, no. 12 (2024): 8962–78. https://doi.org/10.1002/mp.17390.

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AbstractBackgroundDeformable image registration is an essential technique of medical image analysis, which plays important roles in several clinical applications. Existing deep learning‐based registration methods have already achieved promising performance for the registrations with small deformations, while it is still challenging to deal with the large deformation registration due to the limits of the image intensity‐similarity‐based objective function.PurposeTo achieve the image registration with large‐scale deformations, we proposed a multilevel network architecture FCNet to gradually refi

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Ruchti, Alexander, Alexander Neuwirth, Allison K. Lowman, Savannah R. Duenweg, Peter S. LaViolette, and John D. Bukowy. "Homologous point transformer for multi-modality prostate image registration." PeerJ Computer Science 8 (December 1, 2022): e1155. http://dx.doi.org/10.7717/peerj-cs.1155.

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Registration is the process of transforming images so they are aligned in the same coordinate space. In the medical field, image registration is often used to align multi-modal or multi-parametric images of the same organ. A uniquely challenging subset of medical image registration is cross-modality registration—the task of aligning images captured with different scanning methodologies. In this study, we present a transformer-based deep learning pipeline for performing cross-modality, radiology-pathology image registration for human prostate samples. While existing solutions for multi-modality

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Kashiwabara, Kazuma, Keisuke Kazama, and Yoshitaka Marumo. "Performance Evaluation of Image Registration for Map Images." Journal of Robotics and Mechatronics 35, no. 1 (2023): 153–59. http://dx.doi.org/10.20965/jrm.2023.p0153.

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Safety must be guaranteed for the widespread use of automated vehicles. Accurate estimation of the automated vehicle’s self-position is crucial to guarantee the safety of the automated vehicle. In this study, the performance of an image registration method using brightness for the self-position estimation of automated vehicles using 2D map images was evaluated. Moreover, the effect of the difference between the two map images on the image registration was evaluated. Consequently, if a two-dimensional Fourier transform is applied to a map image and the brightness gradient feature is present in

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Hou, Ying, and Gui Cai Wang. "The Automatic Image Registration Algorithm Based on Bag of Words and Features Point." Advanced Materials Research 1049-1050 (October 2014): 1599–602. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1599.

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Abstract (sommario):

According to actual requirements and situation, a novel automatic image registration algorithm was presented based on bag of words and features point. Firstly, pyramid delaminating was used to preprocess the pre-registration images. Then, image normalized could be obtained. Secondly, the dense SIFT features points could be extracted. The paper could get the feature eigenvectors descriptions of image by k-means clustering algorithm. Thirdly, the feature eigenvectors descriptions of image were train and the reference and pre-registration images were classification through Support Vector Machine

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Liu, Xiaojia, Yalin Ding, and Chongyang Liu. "MSIM: A Multiscale Iteration Method for Aerial Image and Satellite Image Registration." Remote Sensing 17, no. 8 (2025): 1423. https://doi.org/10.3390/rs17081423.

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Abstract (sommario):

The registration of aerial images and satellite images is a key step in leveraging complementary information from heterogeneous remote sensing images. Due to the significant intrinsic differences, such as scale, radiometric, and temporal differences, between the two types of images, existing multimodal registration methods tend to be either inaccurate or unstable when applied. This paper proposes a coarse-to-fine registration method for aerial images and satellite images based on the multiscale iteration method (MSIM). Firstly, an image pyramid is established, and feature points are extracted

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Rathore, Gurpreet, and Vijay Dhir. "A comparative approach to image registration methods." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 6, no. 2 (2013): 757–62. http://dx.doi.org/10.24297/ijmit.v6i2.3821.

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Abstract (sommario):

Image processing methods are possibly able to visualize objects inside the human body. Efficient image processing methods are useful in medical diagnosis, treatment planning and medical research. Medical images are used for medical diagnosis. These images should be geometrically aligned for better observation. Registration is necessary technique to integrate data taken from different measurements. Image Registration is a process of overlaying two or more images that can taken at different times, using different devices, different viewpoints and from different angles in order to have 2D or 3D p

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Akram, Muhammad Adeel, Tallha Akram, Umer Javed, Muhammad Rafiq, Mehvish Naz, and Di He. "DeBo: Contrast enhancement for image registration using binary differential evolution and bat optimization." PLOS ONE 19, no. 12 (2024): e0315902. https://doi.org/10.1371/journal.pone.0315902.

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Image registration has demonstrated its significance as an essential tool for target recognition, classification, tracking, and damage assessment during natural catastrophes. The image registration process relies on the identification of numerous reliable features; thus, low resolutions, poor lighting conditions, and low image contrast substantially diminish the number of dependable features available for registration. Contrast stretching enhances image quality, facilitating the object detection process. In this study, we proposed a hybrid binary differential evolution and BAT optimization mod

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Roy, Abhisek, Roy Pranab Kanti, Anirban Mitra, et al. "Artificial bee colony-based nonrigid demons registration." Artificial bee colony-based nonrigid demons registration 14, no. 4 (2024): 3951–61. https://doi.org/10.11591/ijece.v14i4.pp3951-3961.

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Abstract (sommario):

The artificial bee colony (ABC) algorithm has gained popularity in recent years for its ability to solve optimization problems. The accuracy and resilience of ABC-based image processing techniques have demonstrated encouraging outcomes. The ABC method is an excellent solution for image processing issues since it has the ability to swiftly and effectively explore the search space. The current research intends to address image registration issues by refining the existing image registration strategy using ABC algorithm. The process of nonrigid demons registratio

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WANG Yun, 王运, and 颜昌翔 YAN Chang-xiang. "Sub-pixel image registration of spectrometer images." Optics and Precision Engineering 20, no. 3 (2012): 661–67. http://dx.doi.org/10.3788/ope.20122003.0661.

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