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Relevant bibliographies by topics / Image registration

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Academic literature on the topic 'Image registration'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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Journal articles on the topic "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 (February 1, 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 sub-regions is always required in image guided surgery (IGS) and other medical procedures because it considers only the desired objects in medical images instead of the whole image contents. The proposed interested common sub-regions based registration is compared with the two states-of-the-art methods on MR images of human brain. In the experiments of rigid and deformable registrations, we show that our approach outperforms in terms of both the accuracy and time efficiency. The results reveal that interested common sub-region based registration can achieve good performance, regarding both the accuracy as well as the the time efficiency in monomodal brain image registration. In addition, the proposed approach also indicates the potential for multimodal images of different human organs.

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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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Liang, Bo, Xi Chen, Lan Yu, Song Feng, Yangfan Guo, Wenda Cao, Wei Dai, Yunfei Yang, and Ding Yuan. "High-precision Multichannel Solar Image Registration Using Image Intensity." Astrophysical Journal Supplement Series 261, no. 2 (July 20, 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 step gradient descent algorithm as an optimizer to maximize the normalized cross correlation metric. The fine registration step uses the Powell–Brent algorithms as an optimizer and brings the Mattes mutual information similarity metric to the minimum. We selected five pairs of images with different resolutions, rotation angles, and shifts to compare and evaluate our results to those obtained by scale-invariant feature transform and phase correlation. The images are observed by the 1.6 m Goode Solar Telescope at Big Bear Solar Observatory and the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Furthermore, we used the mutual information and registration time criteria to quantify the registration results. The results prove that the proposed method not only reaches better registration precision but also has better robustness. Meanwhile, we want to highlight that the method can also work well for the time-series solar image registration.

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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 (September 16, 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 obtained with CLAIRE to lower resolution registrations for synthetic and real-world imaging datasets. Our results suggest that registration at full resolution can yield a superior registration quality—but not always. For example, downsampling a synthetic image from 10243 to 2563 decreases the Dice coefficient from 92% to 79%. However, the differences are less pronounced for noisy or low contrast high resolution images. CLAIRE allows us not only to register images of clinically relevant size in a few seconds but also to register images at unprecedented resolution in reasonable time. The highest resolution considered are CLARITY images of size 2816×3016×1162. To the best of our knowledge, this is the first study on image registration quality at such resolutions.

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

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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 points on finer levels; meanwhile traditional optimization methods without demanding intelligent optimization algorithms which consume more time can find the accurate registration parameter on finer levels, so registration speed is improved. Experimental results indicate that this method can finish fast and accurate registration for images between which there exists large local difference.

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Bingjian, Wang, Lu Quan, Li Yapeng, Li Fan, Bai Liping, Lu Gang, and Lai Rui. "Image registration method for multimodal images." Applied Optics 50, no. 13 (April 25, 2011): 1861. http://dx.doi.org/10.1364/ao.50.001861.

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Watanabe, Yoichi, and Eunyoung Han. "Image registration accuracy of GammaPlan: a phantom study." Journal of Neurosurgery 109, Supplement (December 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 scanner's axis. The authors attempted image registration of 2 CT image sets, CT and MR image sets, and 2 MR image sets. The accuracy of image registration was evaluated by measuring the x, y, and z coordinate values of the landmarks on each image set after 2 image sets were coregistered. The authors calculated the differences of the x, y, and z values and the distance, d, between corresponding landmarks in 2 image sets. To minimize interobserver dependence of coordinate measurements, 2 physicists did measurements independently. Results The distances, d, averaged over the 9 landmarks, were 2.63 ± 1.64 and 0.95 ± 0.25 mm for CT–CT and MR–MR image registrations, respectively. When the CT images of the air-filled phantom and MR images were coregistered, however, the algorithm performed poorly: d = 13.8 ± 1.23 mm. To remedy this, the authors undertook a 2-step process by first performing landmark-based registration of the 2 image sets and subsequently applying the automatic registration. With this approach, the mean distance drastically improved: d = 0.74 ± 0.31 mm. When the water-filled phantom was used for CT scans, the registration accuracy of CT and MR image sets was acceptable without the 2-step registration process: d = 1.18 ± 0.36 mm. Conclusions The accuracy of automatic registration of image sets from the same modality was within the voxel size of the scanned images. The accuracy of CT–MR image registration strongly depended on whether the phantom for CT scans was filled with air or water. This indicates the significant effect of the amount of common data available for a mutual information-based algorithm on the accuracy.

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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 (September 28, 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 reference curve for the moving pixels. The Gaussian function, which is symmetric about the coordinates of the points of the reference curve, was used to improve the continuity in the intra- and inter-segmented areas. The overall result on curve transformation by means of the Hausdroff distance was 5.820 ± 1.127 pixels on average on several 512 × 512 synthetic images. The proposed method was compared with an ImageJ plugin, namely bUnwarpJ, and a software suite for deformable image registration and adaptive radiotherapy research, namely DIRART, to evaluate the image registration performance. The experimental result shows that the proposed method yielded better image registration performance than its counterparts. On average, the proposed method could reduce the root mean square error from 2970.66 before registration to 1677.94 after registration and can increase the normalized cross-correlation coefficient from 91.87% before registration to 97.40% after registration.

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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 (July 26, 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 each k-combination set (2,000-8,000,000). The mean and standard deviation of the registration were used as final registration for each image pair. Joint entropy was used to validate the output results. Results: An average of 154 (range: 91-212) landmark pairs were selected for each CT/CBCT image pair. The mean standard deviation of the registration output decreased as the k-size increased for all cases. In general, the joint entropy evaluated was found to be lower than results from commercially available software. Of all 58 cases 58.3% of the k=4, 15% of k=8 and 18.3% of k=12 resulted in the better registration using CORRO as compared to 8.3% from a commercial registration software. The minimum joint entropy was determined for one case and found to exist at the estimated registration mean in agreement with the CORRO algorithm. Conclusion: The results demonstrate that CORRO works even in the extreme case of the pelvic anatomy where the CBCT suffers from reduced quality due to increased noise levels. The estimated optimal registration using CORRO was found to be better than commercially available software for all k-sets tested. Additionally, the k-set of 4 resulted in overall best outcomes when compared to k=8 and 12, which is anticipated because k=8 and 12 are more likely to have combinations that affected the accuracy of the registration.

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Dissertations / Theses on the topic "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 RayStation 4.0.1.4 was assessed using a number of evaluation methods and data. The evaluation methods were point of interest (POI) propagation, contour propagation and dose measurements. The data types used were phantom and patient data. A number of metrics were used for quantitative analysis and visual inspection was used for qualitative analysis. The quantitative and qualitative results indicated that all DVFs calculated by the DIR algorithm contained errors which translated into errors in the propagated contours and propagated dose. The results showed that the errors were largest for small contour volumes (<20cm3) and for large anatomical volume changes between the image sets, which pushes the algorithms ability to deform, a significant decrease in accuracy was observed for anatomical volume changes of greater than 10%. When the propagated contours in the head and neck were used for planning the errors in the DVF were found to cause under dosing to the target tumour by up to 32% and over dosing to the organs at risk (OAR) by up to 12% which is clinically significant. The results also indicated that the image quality does not have a significant effect on the DIR algorithms calculations. Dose measurements indicated errors in the DVF calculations that could potentially be clinically significant. The results indicate that contour propagation and dose propagation must be used with caution if clinical use is intended. For clinical use contour propagation requires evaluation of every propagated contour by an expert user and dose propagation requires thorough evaluation of the DVF.

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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 proposed, this is based on the assumption of local sparsity in the pixel motion between two images. Using a coarse and then fine registration, large displacements can be accommodated with a result that is to a sub-pixel precision. The registration process produces a displacement map (DM) between two images. From a sequence of DMs, an estimation of the sensor's motion is made. This is performed by a proposed fast searching and matching technique applied to a library of modelled DMs. Further, this technique exploits regularised splines to estimate the attitude and trajectory of the platform. To validate the results, a mosaic has been produced from three sets of in field data. Using a more detailed model of the acoustic propagation has the potential to improve the results further. As a step towards this a baseband underwater channel model has been developed. A physics simulator is used to characterise the channel at waymark points in a changing environment. A baseband equivalent representation of the time varying channel is then interpolated from these points. Processing in the baseband reduces the sample rate and hence reduces the run time for the model. A comparison to a more established channel model has been made to validate the results.

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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 its transformation, criterion, and search. The transformation is the model of image deformation required for alignment. The criterion is the definition of the best registration. The search describes how the best registration is to be found. This thesis presents two image registration methods; fast robust correlation and orientation correlation. The presented methods find translational transformations. Both define their criterion of the best registration using robust statistics. Fast robust correlation applies robust statistics to pixel intensity differences. Orientation correlation applies robust statistics to differences in orientation of intensity gradient. This gives orientation correlation the property of illumination invariance. Both use an exhaustive search to find the best registration. The novelty of fast robust correlation and orientation correlation is the combination of robust statistics, with an exhaustive search that can be computed quickly with fast Fourier transforms (FFTs). This is achieved by expressing a statistically robust registration surface with correlations. The correlations are computed quickly using FFTs. Computation with FFTs is shown to be particularly advantageous in registration of large images of similar size. Experimental comparisons demonstrate the advantages of the methods over standard correlation-based approaches. Advantage is shown in the experiments of: video coding, video frame registration, tolerance of rotation and zoom, registration of multimodal microscopy images, and face registration.

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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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Books on the topic "Image registration"

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Goshtasby, A. Ardeshir. Image Registration. London: 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. Berlin, Heidelberg: 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. Berlin, Heidelberg: 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. Berlin, Heidelberg: 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. Cham: 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. Cham: 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. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/b11804.

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

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

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Image registration for remote sensing. Cambridge: Cambridge University Press, 2011.

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Book chapters on the topic "Image registration"

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Goshtasby, A. Ardeshir. "Image Descriptors." In Image Registration, 219–46. London: 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, 1–6. London: 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, 401–14. London: 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, 415–34. London: 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, 7–66. London: 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, 67–121. London: 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, 123–217. London: 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, 247–66. London: 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, 267–312. London: 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, 313–41. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2458-0_8.

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Conference papers on the topic "Image registration"

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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. Zurich, Switzerland: 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 model trained to detect damages, which is further mapped on a large image of each bridge component registered using a commercial registration software. The performance of the proposed framework is evaluated on piers of existing bridges, whose external appearance was imaged using a DSLR with a telescopic lens. The results are compared with the conventional visual inspection to analyse the performance and applicability of the proposed framework.</p>

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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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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}. California: 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. Specifically, our approach incorporates a discriminator-free translation network to facilitate the training of the registration network and a patchwise contrastive loss to encourage the translation network to preserve object shapes. Furthermore, we propose to replace an adversarial loss, that is widely used in previous multi-modal image registration methods, with a pixel loss in order to integrate the output of translation into the target modality. This leads to an unsupervised method requiring no ground-truth deformation or pairs of aligned images for training. We evaluate four variants of our approach on the public Learn2Reg 2021 datasets. The experimental results demonstrate that the proposed architecture achieves state-of-the-art performance. Our code is available at https://github.com/heyblackC/DFMIR.

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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 registration. Therefore, in this paper, we propose to combine the demons algorithm and the B-spline method. The new method consists of two steps: First, it applies the B-spline method to deal with the large deformation. Then, it uses the demons algorithm to treat the small deformation. The testing results show that the new method is effective in dealing images with both small and large deformations. Comparing to the demons algorithm as well as the B-spline method, the new method has the smallest registration error and hence, is the best.

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Wu, Yanyan, and Chunhe Gong. "Image Registration for Multimodal Inspection of Mechanical Parts." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/cie-48175.

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Image registration is the process of aligning the corresponding features of images in the same coordinate system. Multimodal registration has been widely used in medical imaging and geographic imaging. However, it has not been broadly applied in the inspection imaging of mechanical parts. Multimodal registration can improve inspection accuracy and quality by combining complementary inspection data from different inspection methods, or “modalities”. The research focus of this work is to develop a computational algorithm to register a CMM point cloud with a CT image in the 2-D (planar) domain. Dealing with outliers is the major concern for achieving required registration accuracy. Targeting solving this problem, a new registration metric is proposed in this work, which makes application of the traditional ICP (Iterative Closest Point) algorithm robust, by optimizing the search for closest points.

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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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Reports on the topic "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, March 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, September 2002. http://dx.doi.org/10.17487/rfc3302.

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Reed, R. A. Comparison of Subpixel Phase Correlation Methods for Image Registration. Fort Belvoir, VA: Defense Technical Information Center, April 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), February 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, September 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, February 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. Fort Belvoir, VA: Defense Technical Information Center, October 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), October 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), August 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. Fort Belvoir, VA: Defense Technical Information Center, May 1989. http://dx.doi.org/10.21236/ada208725.

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