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Journal articles on the topic 'Large baseline image registration'

Author: Grafiati

Published: 12 October 2024

Last updated: 31 July 2025

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1

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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De Backer, Wilfried, Jan De Backer, Ilse Verlinden, et al. "Functional respiratory imaging assessment of glycopyrrolate and formoterol fumarate metered dose inhalers formulated using co-suspension delivery technology in patients with COPD." Therapeutic Advances in Respiratory Disease 14 (January 2020): 175346662091699. http://dx.doi.org/10.1177/1753466620916990.

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Background: Functional respiratory imaging (FRI) is a quantitative postprocessing imaging technique used to assess changes in the respiratory system. Using FRI, we characterized the effects of the long-acting muscarinic antagonist (LAMA), glycopyrrolate metered dose inhaler (GP MDI), and the long-acting β2-agonist (LABA), formoterol fumarate metered dose inhaler (FF MDI), on airway volume and resistance in patients with moderate-to-severe chronic obstructive pulmonary disease. Methods: Patients in this phase IIIb, randomized, double-blind crossover study received twice-daily GP MDI (18 μg) and

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Taguchi, Kayoko, Noriko Numata, Rieko Takanashi, et al. "Clinical Effectiveness and Cost-effectiveness of Videoconference-Based Integrated Cognitive Behavioral Therapy for Chronic Pain: Randomized Controlled Trial." Journal of Medical Internet Research 23, no. 11 (2021): e30690. http://dx.doi.org/10.2196/30690.

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Background Cognitive behavioral therapy is known to improve the management of chronic pain. However, the components of this therapy are still being investigated and debated. Objective This study aimed to examine the effectiveness of an integrated cognitive behavioral therapy program with new components (attention-shift, memory work, video feedback, and image training) delivered via videoconferencing. Methods This study was unblinded and participants were recruited and assessed face-to-face in the outpatient department. We conducted a randomized controlled trial for chronic pain to compare 16 w

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Bukhari, Ali, Vivek Kesari, Reza Sirous, et al. "Increased Cortical Glycolysis Following CD19 CART Therapy: A Radiographic Surrogate for an Altered Blood-Brain Barrier." Blood 134, Supplement_1 (2019): 4454. http://dx.doi.org/10.1182/blood-2019-125794.

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Background: Cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS) are known complications of chimeric antigen receptor T-cell (CAR-T) therapy. These clinical syndromes develop as a result of CAR-T activation, proliferation, and tumor lysis with resultant cytokine secretion. In prior reports of CD19 CAR-T therapy patients, those who developed ICANS showed evidence of endothelial activation and disruption of the blood-brain barrier as a result of cytokine release while only approximately one-third demonstrated changes on Brain MRI (Gust et al. Cancer

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Nigicser, Isabel, Fredrik Identeg, Mikael Sansone, et al. "Do Swedish rock-climbers exhibit more eating disorder and body dissatisfaction symptoms than non-climbers? A cross-sectional study." BMJ Open 14, no. 10 (2024): e085265. http://dx.doi.org/10.1136/bmjopen-2024-085265.

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ObjectivesThe inclusion of rock-climbing in the Olympic Games has increased participation in the sport and attention to athletes' health. In sports where the importance of low body weight is considerate, there is an increased risk of developing eating disorders (EDs). There is sparse research on EDs among rock-climbers. The primary aim was to map ED symptoms among rock-climbers compared with controls. The secondary aim was to analyze differences in body dissatisfaction. Comparisons between rock-climbing levels and sexes were performed.DesignThis is a cross-sectional study in a larger prospecti

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Chen, Chong, and Ozan Öktem. "Indirect Image Registration with Large Diffeomorphic Deformations." SIAM Journal on Imaging Sciences 11, no. 1 (2018): 575–617. http://dx.doi.org/10.1137/17m1134627.

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Fischer, Bernd, and Jan Modersitzki. "Large scale problems arising from image registration." GAMM-Mitteilungen 27, no. 2 (2004): 104–20. http://dx.doi.org/10.1002/gamm.201490006.

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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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Baldwin, D. G., and W. J. Emery. "A systematized approach to AVHRR image navigation." Annals of Glaciology 17 (1993): 414–20. http://dx.doi.org/10.3189/s0260305500013185.

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Accurate co-location and geo-registration of AVHRR (Advanced Very High Resolution Radiometer) is necessary for most applications involving these data. To obtain the necessary accuracy, most of the current systems available for AVHRR image rectification require corrections to errors arising from several different sources. This paper presents a software package which is able to correct registration errors from all sources using effective roll, pitch, and yaw values determined from ground control point offsets. These effective values are used to establish a baseline registration against which oth

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Baldwin, D. G., and W. J. Emery. "A systematized approach to AVHRR image navigation." Annals of Glaciology 17 (1993): 414–20. http://dx.doi.org/10.1017/s0260305500013185.

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Accurate co-location and geo-registration of AVHRR (Advanced Very High Resolution Radiometer) is necessary for most applications involving these data. To obtain the necessary accuracy, most of the current systems available for AVHRR image rectification require corrections to errors arising from several different sources. This paper presents a software package which is able to correct registration errors from all sources using effective roll, pitch, and yaw values determined from ground control point offsets. These effective values are used to establish a baseline registration against which oth

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Haines, Rachel H., Kim S. Thomas, Alan A. Montgomery, et al. "Home interventions and light therapy for the treatment of vitiligo (HI-Light Vitiligo Trial): study protocol for a randomised controlled trial." BMJ Open 8, no. 4 (2018): e018649. http://dx.doi.org/10.1136/bmjopen-2017-018649.

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Introduction Vitiligo is a condition resulting in white patches on the skin. People with vitiligo can suffer from low self-esteem, psychological disturbance and diminished quality of life. Vitiligo is often poorly managed, partly due to lack of high-quality evidence to inform clinical care. We describe here a large, independent, randomised controlled trial (RCT) assessing the comparative effectiveness of potent topical corticosteroid, home-based hand-held narrowband ultraviolet B-light (NB-UVB) or combination of the two, for the management of vitiligo. Methods and analysis The HI-Light Vitilig

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Foskey, Mark, Brad Davis, Lav Goyal, et al. "Large deformation three-dimensional image registration in image-guided radiation therapy." Physics in Medicine and Biology 50, no. 24 (2005): 5869–92. http://dx.doi.org/10.1088/0031-9155/50/24/008.

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Huang, Xishi, Jing Ren, Anwar Abdalbari, and Mark Green. "Deformable image registration for tissues with large displacements." Journal of Medical Imaging 4, no. 1 (2017): 014001. http://dx.doi.org/10.1117/1.jmi.4.1.014001.

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Li, Xiangguo. "High-Accuracy Subpixel Image Registration With Large Displacements." IEEE Transactions on Geoscience and Remote Sensing 55, no. 11 (2017): 6265–76. http://dx.doi.org/10.1109/tgrs.2017.2724303.

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Wang, Chang, Qiongqiong Ren, Xin Qin, and Yi Yu. "Adaptive Diffeomorphic Multiresolution Demons and Their Application to Same Modality Medical Image Registration with Large Deformation." International Journal of Biomedical Imaging 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/7314612.

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Diffeomorphic demons can guarantee smooth and reversible deformation and avoid unreasonable deformation. However, the number of iterations needs to be set manually, and this greatly influences the registration result. In order to solve this problem, we proposed adaptive diffeomorphic multiresolution demons in this paper. We used an optimized framework with nonrigid registration and diffeomorphism strategy, designed a similarity energy function based on grey value, and stopped iterations adaptively. This method was tested by synthetic image and same modality medical image. Large deformation was

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Zhao, Yinsen, Farong Gao, Jun Yu, Xing Yu, and Zhangyi Yang. "Underwater Image Mosaic Algorithm Based on Improved Image Registration." Applied Sciences 11, no. 13 (2021): 5986. http://dx.doi.org/10.3390/app11135986.

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In order to obtain panoramic images in a low contrast underwater environment, an underwater panoramic image mosaic algorithm based on image enhancement and improved image registration (IIR) was proposed. Firstly, mixed filtering and sigma filtering are used to enhance the contrast of the original image and de-noise the image. Secondly, scale-invariant feature transform (SIFT) is used to detect image feature points. Then, the proposed IIR algorithm is applied to image registration to improve the matching accuracy and reduce the matching time. Finally, the weighted smoothing method is used for i

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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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Cocianu, Cătălina-Lucia, Cristian Răzvan Uscatu, and Alexandru Daniel Stan. "Evolutionary Image Registration: A Review." Sensors 23, no. 2 (2023): 967. http://dx.doi.org/10.3390/s23020967.

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Image registration is one of the most important image processing tools enabling recognition, classification, detection and other analysis tasks. Registration methods are used to solve a large variety of real-world problems, including remote sensing, computer vision, geophysics, medical image analysis, surveillance, and so on. In the last few years, nature-inspired algorithms and metaheuristics have been successfully used to address the image registration problem, becoming a solid alternative for direct optimization methods. The aim of this paper is to investigate and summarize a series of stat

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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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Zhao, Liya, and Kebin Jia. "Deep Adaptive Log-Demons: Diffeomorphic Image Registration with Very Large Deformations." Computational and Mathematical Methods in Medicine 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/836202.

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This paper proposes a new framework for capturing large and complex deformation in image registration. Traditionally, this challenging problem relies firstly on a preregistration, usually an affine matrix containing rotation, scale, and translation and afterwards on a nonrigid transformation. According to preregistration, the directly calculated affine matrix, which is obtained by limited pixel information, may misregistrate when large biases exist, thus misleading following registration subversively. To address this problem, for two-dimensional (2D) images, the two-layer deep adaptive registr

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Sun, Wanfeng, Haibo Gao, and Cheng Li. "A Two-Stage Registration Strategy for Thermal–Visible Images in Substations." Applied Sciences 14, no. 3 (2024): 1158. http://dx.doi.org/10.3390/app14031158.

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The analysis of infrared video images is becoming one of the methods used to detect thermal hazards in many large-scale engineering sites. The fusion of infrared thermal imaging and visible image data in the target area can help people to identify and locate the fault points of thermal hazards. Among them, a very important step is the registration of thermally visible images. However, the direct registration of images with large-scale differences may lead to large registration errors or even failure. This paper presents a novel two-stage thermal–visible-image registration strategy specifically

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Li, Kai, Yongsheng Zhang, Zhenchao Zhang, and Guangling Lai. "A Coarse-to-Fine Registration Strategy for Multi-Sensor Images with Large Resolution Differences." Remote Sensing 11, no. 4 (2019): 470. http://dx.doi.org/10.3390/rs11040470.

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Automatic image registration for multi-sensors has always been an important task for remote sensing applications. However, registration for images with large resolution differences has not been fully considered. A coarse-to-fine registration strategy for images with large differences in resolution is presented. The strategy consists of three phases. First, the feature-base registration method is applied on the resampled sensed image and the reference image. Edge point features acquired from the edge strength map (ESM) of the images are used to pre-register two images quickly and robustly. Seco

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Heiselman, Jon Stanley, Natally Horvat, Burcin Agridag-Upcinar, et al. "Inter-reader variability of imaging biomarkers for therapeutic response in pancreatic ductal adenocarcinoma." Journal of Clinical Oncology 42, no. 3_suppl (2024): 700. http://dx.doi.org/10.1200/jco.2024.42.3_suppl.700.

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700 Background: Indeterminate borders of pancreatic ductal adenocarcinoma (PDAC) can impair reliability of lesion annotation in imaging assessment of therapeutic response (TR) after neoadjuvant chemotherapy (NAT). Previous work showed that longitudinal image registration algorithms can track treatment-related changes and produce quantitative imaging biomarkers (QIB) for TR that predict overall (OS) and recurrence-free survival (RFS). We compare inter-reader agreements of these biomarkers against conventional RECIST and tumor volume QIB. Methods: N=30 patients enrolled in a Phase II clinical tr

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Xie, J., X. Lv, C. Chu, et al. "AN IMPROVED IMAGE REGISTRATION ALGORITHM FOR THERMAL INFRARED AND PANCHROMATIC IMAGE BASED ON GEOMETRIC STRUCTURAL PROPERTIES." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-1/W1-2023 (December 5, 2023): 145–52. http://dx.doi.org/10.5194/isprs-annals-x-1-w1-2023-145-2023.

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Abstract. In order to improve the image registration accuracy of heterogeneous remote sensing images with large radiation differences, an improved image registration algorithm for thermal infrared and panchromatic images is proposed. This method uses the phase consistency of intensity and direction to construct a geometric structure feature descriptor called the Histogram of Oriented Phase Congruency (HOPC) for image registration. It employs the Curvature Scale Space (CSS) corner detection to concentrate and extract feature points in contour areas, and defines a similarity metric (called HOPCn

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Wang, Chengjia, Guang Yang, and Giorgos Papanastasiou. "Unsupervised Image Registration towards Enhancing Performance and Explainability in Cardiac and Brain Image Analysis." Sensors 22, no. 6 (2022): 2125. http://dx.doi.org/10.3390/s22062125.

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Magnetic Resonance Imaging (MRI) typically recruits multiple sequences (defined here as “modalities”). As each modality is designed to offer different anatomical and functional clinical information, there are evident disparities in the imaging content across modalities. Inter- and intra-modality affine and non-rigid image registration is an essential medical image analysis process in clinical imaging, as for example before imaging biomarkers need to be derived and clinically evaluated across different MRI modalities, time phases and slices. Although commonly needed in real clinical scenarios,

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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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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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Si, Zhichao. "Unordered Image Stitching Based on the Registration Index Table." Journal of Physics: Conference Series 2303, no. 1 (2022): 012076. http://dx.doi.org/10.1088/1742-6596/2303/1/012076.

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Abstract In order to solve the problem of large amount of calculation and time-consuming in the process of unordered image stitching, we propose an image stitching algorithm based on the image registration index table in this paper. Firstly, we construct the matching index sequence based on the similarity of the matching area. Then we search for image matching pairs according to the matching index sequence, carry out image registration and stitch directly, and generate the registration index table. For image stitching, we build a binary tree model assisted by the image registration index table

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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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Huo, Chunlei, Chunhong Pan, Leigang Huo, and Zhixin Zhou. "Multilevel SIFT Matching for Large-Size VHR Image Registration." IEEE Geoscience and Remote Sensing Letters 9, no. 2 (2012): 171–75. http://dx.doi.org/10.1109/lgrs.2011.2163491.

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Beg, Mirza Faisal, and Ali Khan. "Symmetric Data Attachment Terms for Large Deformation Image Registration." IEEE Transactions on Medical Imaging 26, no. 9 (2007): 1179–89. http://dx.doi.org/10.1109/tmi.2007.898813.

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Yang, Han, Xiaorun Li, Liaoying Zhao, and Shuhan Chen. "A Novel Coarse-to-Fine Scheme for Remote Sensing Image Registration Based on SIFT and Phase Correlation." Remote Sensing 11, no. 15 (2019): 1833. http://dx.doi.org/10.3390/rs11151833.

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Automatic image registration has been wildly used in remote sensing applications. However, the feature-based registration method is sometimes inaccurate and unstable for images with large scale difference, grayscale and texture differences. In this manuscript, a coarse-to-fine registration scheme is proposed, which combines the advantage of feature-based registration and phase correlation-based registration. The scheme consists of four steps. First, feature-based registration method is adopted for coarse registration. A geometrical outlier removal method is applied to improve the accuracy of c

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Roy-Chowdhury, A. K., R. Chellappa, and T. Keaton. "Wide Baseline Image Registration With Application to 3-D Face Modeling." IEEE Transactions on Multimedia 6, no. 3 (2004): 423–34. http://dx.doi.org/10.1109/tmm.2004.827511.

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Lamprinou, Nefeli, Nikolaos Nikolikos, and Emmanouil Z. Psarakis. "Groupwise Image Alignment via Self Quotient Images." Sensors 20, no. 8 (2020): 2325. http://dx.doi.org/10.3390/s20082325.

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Compared with pairwise registration, the groupwise one is capable of handling a large-scale population of images simultaneously in an unbiased way. In this work we improve upon the state-of-the-art pixel-level, Least-Squares (LS)-based groupwise image registration methods. Specifically, the registration technique is properly adapted by the use of Self Quotient Images (SQI) in order to become capable for solving the groupwise registration of photometrically distorted, partially occluded as well as unimodal and multimodal images. Moreover, the proposed groupwise technique is linear to the cardin

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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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Lu, Junyan, Hongguang Jia, Tie Li, Zhuqiang Li, Jingyu Ma, and Ruifei Zhu. "An Instance Segmentation Based Framework for Large-Sized High-Resolution Remote Sensing Images Registration." Remote Sensing 13, no. 9 (2021): 1657. http://dx.doi.org/10.3390/rs13091657.

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Feature-based remote sensing image registration methods have achieved great accomplishments. However, they have faced some limitations of applicability, automation, accuracy, efficiency, and robustness for large high-resolution remote sensing image registration. To address the above issues, we propose a novel instance segmentation based registration framework specifically for large-sized high-resolution remote sensing images. First, we design an instance segmentation model based on a convolutional neural network (CNN), which can efficiently extract fine-grained instances as the deep features f

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Wu, Shu Guang, Shu He, and Xia Yang. "The Application of SIFT Method towards Image Registration." Advanced Materials Research 1044-1045 (October 2014): 1392–96. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.1392.

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The scale invariant features transform (SIFT) is commonly used in object recognition,According to the problems of large memory consumption and low computation speed in SIFT (Scale Invariant Feature Transform) algorithm.During the image registration methods based on point features,SIFT point feature is invariant to image scale and rotation, and provides robust matching across a substantial range of affine distortion. Experiments show that on the premise that registration accuracy is stable, the proposed algorithm solves the problem of high requirement of memory and the efficiency is improved gr

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Zhang, Shuang, Lichun Sui, Rongrong Zhou, Zhangyuan Xun, Chengyan Du, and Xiao Guo. "Mountainous SAR Image Registration Using Image Simulation and an L2E Robust Estimator." Sustainability 14, no. 15 (2022): 9315. http://dx.doi.org/10.3390/su14159315.

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Synthetic Aperture Radar (SAR) is one of the most widely utilized methods to extract elevation information and identify large-scale deformations in mountainous areas. Homologous points in stereo SAR image pairs are difficult to identify due to complex geometric and radiometric distortions. In this paper, a new approach for mountainous area images is suggested. Firstly, a simulated SAR image and a look-up table based on DEM data are generated by a range-Doppler model and an empirical formula. Then, a point matching RPM-L2E algorithm is used to match images obtained by the simulation and in real

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Scherf, N., J. Einenkel, L. C. Horn, et al. "Large Histological Serial Sections for Computational Tissue Volume Reconstruction." Methods of Information in Medicine 46, no. 05 (2007): 614–22. http://dx.doi.org/10.1160/me9065.

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Summary Objectives: A proof of principle study was conducted for microscopic tissue volume reconstructions using a new image processing chain operating on alternately stained large histological serial sections. Methods: Digital histological images were obtained from conventional brightfield transmitted light microscopy. A powerful nonparametric nonlinear optical flow-based registration approach was used. In order to apply a simple but computationally feasible sum-of-squared-differences similarity measure even in case of differing histological stainings, a new consistent tissue segmentation pro

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Jiang, Ji Tong, Chao Song, and Jun An. "Study on Infrared Thermography Mosaic Algorithm Based on the Feature Point Detection and Registration." Advanced Materials Research 594-597 (November 2012): 1138–42. http://dx.doi.org/10.4028/www.scientific.net/amr.594-597.1138.

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When detecting the large objects by infrared thermal method, it is difficult to get a whole panoramic picture. So it needs to stitch some infrared thermography. Image mosaic includes 4 steps, feature detection, feature registration, image transformation and image fusion. This paper studies about an infrared thermograph mosaic algorithm based on the feature point detection and registration, and realizes it in MATLAB.

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Adelman, Zeev, and Leo Joskowicz. "Deformable registration and region-of-interest image reconstruction in sparse repeat CT scanning." Journal of X-Ray Science and Technology 28, no. 6 (2020): 1069–89. http://dx.doi.org/10.3233/xst-200706.

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BACKGROUND: Repeat CT scanning is ubiquitous in many clinical situations, e.g. to follow disease progression, to evaluate treatment efficacy, and to monitor interventional CT procedures. However, it incurs in cumulative radiation to the patient which can be significantly reduced by using a region of interest (ROI) and the existing baseline scan. OBJECTIVE: To obtain a high-quality reconstruction of a ROI with a significantly reduced X-ray radiation dosage that accounts for deformations. METHODS: We present a new method for deformable registration and image reconstruction inside an ROI in repea

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Luo, Xin, Zuqi Wei, Yuwei Jin, et al. "Fast Automatic Registration of UAV Images via Bidirectional Matching." Sensors 23, no. 20 (2023): 8566. http://dx.doi.org/10.3390/s23208566.

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Image registration plays a vital role in the mosaic process of multiple UAV (Unmanned Aerial Vehicle) images acquired from different spatial positions of the same scene. Aimed at the problem that many fast registration methods cannot provide both high speed and accuracy simultaneously for UAV visible light images, this work proposes a novel registration framework based on a popular baseline registration algorithm, ORB—the Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elemental Features) algorithm. First, the ORB algorithm is utilized to ext

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