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Добірка наукової літератури з теми "Image guide"

Автор: Grafiati
Опубліковано: 28 квітня 2023

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Статті в журналах з теми "Image guide"

1

Berry, E., M. Cuppone, S. Porada, P. A. Millner, A. Rao, N. Chiverton, and B. B. Seedhom. "Personalised image-based templates for intra-operative guidance." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 219, no. 2 (February 1, 2005): 111–18. http://dx.doi.org/10.1243/095441105x9273.

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Анотація:
The high rate of unplanned perforation, poor fixation, and nerve injury with freehand pedicle screw insertion has led to the use of image-guided navigation systems. Although these improve accuracy, they have several drawbacks that could be overcome by using image-based drilling guide templates. The accuracy of such templates was tested in a cadaveric study of screw placement in the lumbar, thoracic, and cervical regions of the spine. The dimensional stability with autoclaving of duraform polyamide, to be used for manufacturing the guides, was first determined using test specimens. Computed tomography (CT) images were acquired of 4 cadaveric spines, and placement of 4 cervical, 32 thoracic, and 14 lumbar screws was planned. Eighteen personalized drilling guide templates, in four different designs, were built. Orthopaedic surgeons experienced in the freehand techniques used the templates. CT images were acquired to assess placement position with respect to the pedicle. Duraform polyamide was found to be unaffected by sterilization. Two of the template designs facilitated the placement of 20/20 screws without error. Templates can lead to successful screw placement, even in small pedicles, providing their design is optimized for the application area, e.g. with enhanced rotational stabilization.
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2

Li, Zhan, Jianhang Zhang, Ruibin Zhong, Bir Bhanu, Yuling Chen, Qingfeng Zhang, and Haoqing Tang. "Lightweight and Efficient Image Dehazing Network Guided by Transmission Estimation from Real-World Hazy Scenes." Sensors 21, no. 3 (February 1, 2021): 960. http://dx.doi.org/10.3390/s21030960.

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In this paper, a transmission-guided lightweight neural network called TGL-Net is proposed for efficient image dehazing. Unlike most current dehazing methods that produce simulated transmission maps from depth data and haze-free images, in the proposed work, guided transmission maps are computed automatically using a filter-refined dark-channel-prior (F-DCP) method from real-world hazy images as a regularizer, which facilitates network training not only on synthetic data, but also on natural images. A double-error loss function that combines the errors of a transmission map with the errors of a dehazed image is used to guide network training. The method provides a feasible solution for introducing priors obtained from traditional non-learning-based image processing techniques as a guide for training deep neural networks. Extensive experimental results demonstrate that, in terms of several reference and non-reference evaluation criteria for real-world images, the proposed method can achieve state-of-the-art performance with a much smaller network size and with significant improvements in efficiency resulting from the training guidance.
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3

Ishizaka, Hiroshi. "Directable Needle Guide: Efficacy for Image-Guided Percutaneous Interventions." ISRN Radiology 2013 (November 27, 2013): 1–3. http://dx.doi.org/10.5402/2013/516941.

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Анотація:
Diagnostic and therapeutic image-guided percutaneous interventions have become increasingly important in the clinical management of various conditions. Though precise needle placement via a safe route is essential for successful percutaneous interventions, it is often difficult in cases of deeply situated, small lesions. The present paper describes the efficacy of the directable needle guide (DNG), which allows manipulation of the direction of a fine needle within organs. The DNG was used in patients for needle biopsy of hepatic (n=26) and splenic (n=1) lesions and for percutaneous ethanol injection therapy for liver tumors (n=33) under sonographic or computed tomography guidance. The DNG enabled the direction of a 21- or 22-gauge needle to be successfully changed during needle advancement in all cases, allowing adjustment of the location of the needle tip or needle access root to avoid vessels, the gallbladder, and the lungs. We conclude that DNG increases the safety and ease of percutaneous interventions.
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4

Zhang, Yuan, Peng Wang, Ji Xiang Li, Shi Wei Yin, and Jing Lei Xin. "Path Segmentation Algorithm for Automatic Guided Vehicle Based on Machine Vision." Key Engineering Materials 431-432 (March 2010): 330–33. http://dx.doi.org/10.4028/www.scientific.net/kem.431-432.330.

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A novel path segmentation algorithm for automatic guided vehicle is designed by machine vision technology. Firstly, wiener filter based on parameter estimate is used to restore path image in HIS model. Secondly, mutivariate stepwise fitted regression method is adopted to smooth guide line edge of path image. It can be concluded that path guide line of automatic guided vehicle under industry surrounding may be recognized accurately through this way.
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5

Karthikesh, Madhumithra S., and Xinmai Yang. "Photoacoustic image-guided interventions." Experimental Biology and Medicine 245, no. 4 (November 20, 2019): 330–41. http://dx.doi.org/10.1177/1535370219889323.

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Анотація:
Photoacoustic imaging has demonstrated its potential for diagnosis over the last few decades. In recent years, its unique imaging capabilities, such as detecting structural, functional and molecular information in deep regions with optical contrast and ultrasound resolution, have opened up many opportunities for photoacoustic imaging to be used during image-guided interventions. Numerous studies have investigated the capability of photoacoustic imaging to guide various interventions such as drug delivery, therapies, surgeries, and biopsies. These studies have demonstrated that photoacoustic imaging can guide these interventions effectively and non-invasively in real-time. In this minireview, we will elucidate the potential of photoacoustic imaging in guiding active and passive drug deliveries, photothermal therapy, and other surgeries and therapies using endogenous and exogenous contrast agents including organic, inorganic, and hybrid nanoparticles, as well as needle-based biopsy procedures. The advantages of photoacoustic imaging in guided interventions will be discussed. It will, therefore, show that photoacoustic imaging has great potential in real-time interventions due to its advantages over current imaging modalities like computed tomography, magnetic resonance imaging, and ultrasound imaging. Impact statement Photoacoustic imaging is an emerging modality for use in image-guided interventional procedures. This imaging technology has a unique ability to offer real-time, non-invasive, cost-effective, and radiation-free guidance in a real-world operating environment. This is substantiated in this article which sums up the current state and underlines promising results of research using photoacoustic imaging in guiding drug delivery, therapy, surgery, and biopsy. Hence, this minireview facilitates future research and real-world application of photoacoustic image-guided interventions.
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6

Ji, Jingyu, Yuhua Zhang, Zhilong Lin, Yongke Li, Changlong Wang, Yongjiang Hu, Fuyu Huang, and Jiangyi Yao. "Fusion of Infrared and Visible Images Based on Optimized Low-Rank Matrix Factorization with Guided Filtering." Electronics 11, no. 13 (June 26, 2022): 2003. http://dx.doi.org/10.3390/electronics11132003.

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In recent years, image fusion has been a research hotspot. However, it is still a big challenge to balance the problems of noiseless image fusion and noisy image fusion. In order to improve the weak performance and low robustness of existing image fusion algorithms in noisy images, an infrared and visible image fusion algorithm based on optimized low-rank matrix factorization with guided filtering is proposed. First, the minimized error reconstruction factorization is introduced into the low-rank matrix, which effectively enhances the optimization performance, and obtains the base image with good filtering performance. Then using the base image as the guide image, the source image is decomposed into the high-frequency layer containing detail information and noise, and the low-frequency layer containing energy information through guided filtering. According to the noise intensity, the sparse reconstruction error is adaptively obtained to fuse the high-frequency layers, and the weighted average strategy is utilized to fuse the low-frequency layers. Finally, the fusion image is obtained by reconstructing the pre-fused high-frequency layer and the pre-fused low-frequency layer. The comparative experiments show that the proposed algorithm not only has good performance for noise-free images, but more importantly, it can effectively deal with the fusion of noisy images.
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7

Li, Jinfeng. "Low-light image enhancement with contrast regularization." Frontiers in Computing and Intelligent Systems 1, no. 3 (October 19, 2022): 25–28. http://dx.doi.org/10.54097/fcis.v1i3.2022.

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Анотація:
Because the processing of existing low-light images undergoes multiple sampling processing, there is serious information degradation, and only clear images are used as positive samples to guide network training, low-light image enhancement processing is still a challenging and unsettled problem. Therefore, a multi-scale contrast learning low-light image enhancement network is proposed. First, the image generates rich features through the input module, and then the features are imported into a multi-scale enhancement network with dense residual blocks, using positive and negative samples to guide the network training, and finally using the refinement module to enrich the image details. Experimental results on the dataset show that this method can reduce noise and artifacts in low-light images, and can improve contrast and brightness, demonstrating its advantages.
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8

Chen, Ying, Dihe Huang, Shang Xu, Jianlin Liu, and Yong Liu. "Guide Local Feature Matching by Overlap Estimation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 365–73. http://dx.doi.org/10.1609/aaai.v36i1.19913.

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Local image feature matching under large appearance, viewpoint, and distance changes is challenging yet important. Conventional methods detect and match tentative local features across the whole images, with heuristic consistency checks to guarantee reliable matches. In this paper, we introduce a novel Overlap Estimation method conditioned on image pairs with TRansformer, named OETR, to constrain local feature matching in the commonly visible region. OETR performs overlap estimation in a two step process of feature correlation and then overlap regression. As a preprocessing module, OETR can be plugged into any existing local feature detection and matching pipeline, to mitigate potential view angle or scale variance. Intensive experiments show that OETR can boost state of the art local feature matching performance substantially, especially for image pairs with small shared regions. The code will be publicly available at https://github.com/AbyssGaze/OETR.
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9

Gao, Guxue, Huicheng Lai, and Zhenhong Jia. "Unsupervised Image Dedusting via a Cycle-Consistent Generative Adversarial Network." Remote Sensing 15, no. 5 (February 27, 2023): 1311. http://dx.doi.org/10.3390/rs15051311.

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In sand–dust weather, the quality of the image is seriously degraded, which affects the ability of advanced applications to image using remote sensing. To improve the image quality and enhance the performance of image dedusting, we propose an end-to-end cyclic generative adversarial network (D-CycleGAN) for image dedusting, which does not require pairs of sand–dust images and corresponding ground truth images for training. In other words, we train the network in an unpaired way. Specifically, we designed a jointly optimized guided module (JOGM), comprised of the sandy guided synthesis module (SGSM) and the clean guided synthesis module (CGSM), which aim to jointly guide the generator through corresponding discriminator adversarials to reduce the color distortion and artifacts. JOGM can significantly improve image quality. We propose a network hidden layer adversarial branch to perform adversarials from inside the network, which better supervises the hidden layer to further improve the quality of the generated images. In addition, we improved the original CycleGAN loss function and propose a dual-scale semantic perception loss in feature space and a color identity-preserving loss in pixel space to constrain the network. Extensive experiments demonstrate that our proposed network model effectively removes sand dust, has better clarity and image quality, and outperforms the state-of-the-art techniques. In addition, the proposed method can help the target detection algorithm to improve its detection accuracy and capability, and our method generalizes well to the enhancement of underwater images and hazy images.
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10

Safirin Karis, Mohd, Nursabillilah Mohd Ali, Mohd Azamuddin Ali, Muhamad Raimi Sadiq Samsudin, Nurasmiza Selamat, Wira Hidayat Mohd Saad, Amar Faiz Zainal Abidin, and Zairi Ismael Rizman. "Guide Sign Analysis of Traffic Sign Data-Set Using Supervised Spiking Neuron Technique." International Journal of Engineering & Technology 7, no. 3.14 (July 25, 2018): 221. http://dx.doi.org/10.14419/ijet.v7i3.14.16897.

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In this paper, 20 guided traffic signs mostly displayed around Malacca area were selected as project databased. Early hypothesis was made as the error for each usable image will increased as more interference introduced to the original image used. Three types of conditions which are hidden region, image brightness and image rotation were selected as an experiment to analyze the performance of each sign used. Each condition will perform a specific error to generate their mean value and in the same, image recognition will take place in the matchup process. By focusing on the result, it produces hidden region critically ascending mean error value at 62.5% = 0.07 and has average value at others points. For image brightness effect, it shows a higher mean error value collected at less brightness points and non-stable pattern at 10% to 60% brightness. As for rotation upshot, the values show a critically ascending for error value at 22.5% and slightly increase at 2% to 5% rotation point. For the recognition process, at 6.25% hidden region, almost 70% of images are correctly matched to its own classes while at 62.5% hidden region only 40% of images are correctly matched to its own classes and leaving 2 images to outperform. For -40% brightness, 45% of images are correctly matched to its own classes while at 60% brightness 65% of images are correctly matched to its own classes and leaving 1 image to outperform. Lastly, at 2.5 degree rotation, 85% of images are correctly matched to its own classes while at 25° rotation, 45% of images are correctly matched to its own classes and leaving 2 images to outperform. Finally, the error forms will affect the final output response of the detected traffic signs used.
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Дисертації з теми "Image guide"

1

Goodenow, Daniel P. "A reference guide to JPEG compression /." Online version of thesis, 1993. http://hdl.handle.net/1850/11714.

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Dean, Chad E. Vosters Nathan P. "Update of the Navy Contract Writing Guide /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Dec%5FDean%5FMBA.pdf.

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Thesis (M.B.A.)--Naval Postgraduate School, December 2003.
"MBA professional report"--Cover. Thesis advisor(s): E. Marshall Engelbeck, Jeffrey Cuskey. Includes bibliographical references (p. 19-22). Also available online.
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3

Al-Zoubi, Asem S. "Rectangular dielectric resonator antennas fed by dielectric image guide /." Full text available from ProQuest UM Digital Dissertations, 2008. http://0-proquest.umi.com.umiss.lib.olemiss.edu/pqdweb?index=0&did=1799142881&SrchMode=1&sid=4&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1257878793&clientId=22256.

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Thesis (Ph.D.)--University of Mississippi, 2008.
Typescript. Vita. "December 2008." Dissertation director: Ahmed A. Kishk Includes bibliographical references (leaves 142-148). Also available online via ProQuest to authorized users.
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4

Dobbs, Andrew L. "A program manager's guide for software cost estimating." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02Dec%5FDobbs.pdf.

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5

Ko, Eunbyol. "Music and Image a performer's guide to Maurice Ravel's Miroirs /." Cincinnati, Ohio : University of Cincinnati, 2007. http://www.ohiolink.edu/etd/view.cgi?acc%5Fnum=ucin1172781745.

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Анотація:
Thesis (Dr. of Musical Arts)--University of Cincinnati, 2007.
Title from electronic thesis title page (viewed Apr.3, 2007). Includes abstract. Keywords: Ravel; Miroirs; Performer's Guide; Image; Impressionism; Symbolism; Eunbyol Ko Includes bibliographical references.
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6

Isbel, Joanne. ""The good friend guide" : a group social skills intervention /." [St. Lucia, Qld.], 2004. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18047.pdf.

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7

Paul, Dominique-Lynda. "Contribution à la modélisation des principaux éléments passifs en technologie guide image." Brest, 1990. http://www.theses.fr/1990BRES2002.

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Ce travail a pour objet l'etude et la modelisation des elements passifs de base intervenant dans la realisation de filtres en technologie guide image. Ainsi, apres avoir examine les approches actuellement disponibles concernant cette structure, l'auteur s'est interesse a la modelisation de resonateurs annulaires fonctionnant en modes hybrides d'ordres azimutaux eleves et plus particulierement au probleme du couplage entre ces deux structures. Si les modeles utilises concordent tres bien avec l'experience dans la bande basse des hyperfrequences (bande x), l'etude experimentale en ondes millimetriques a montre leur imprecision relative. La derniere section a donc objet de proposer quelques ameliorations a apporter a ces approches ouvrant ainsi la voie a des travaux futurs
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8

Ko, Eunbyol. "Music and Image: A Performer’s Guide to Maurice Ravel’s Miroirs." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1172781745.

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9

Toutain, Serge. "Contribution à l'étude du guide image mise en évidence d'ondes à fuites." Lille 1, 1986. http://www.theses.fr/1986LIL10157.

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Анотація:
Etude critique des différentes approches théoriques et proposition d'un nouveau modèle base sur la caractérisation de discontinuités en guide ouvert en association avec la méthode de la résonance transverse. Cette méthode montre la possibilité de création d'ondes de fuite et explicite les limitations d'utilisation liées à ce phénomène. Etude de sous-ensembles tels que coupleurs de proximité et résonateurs diélectriques tubulaires
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10

Toutain, Serge. "Contribution à l'étude du guide image mise en évidence d'ondes à fuites /." Grenoble 2 : ANRT, 1986. http://catalogue.bnf.fr/ark:/12148/cb37601616n.

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Книги з теми "Image guide"

1

Center, Burchfield-Penney Art, and Holmes Elementary School (Town of Tonawanda, N.Y.), eds. Image & word: Curriculum guide. Buffalo, N.Y: Burchfield-Penney Art Center, 1998.

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2

Miller, Howard W. Image processing system guide. Byfield, Mass. (136 Orchard St., Byfield 01922-1605): Information Technology Institute, 1994.

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3

Electronic image communications: A guide to networking image files. Medford, NJ: Learned Information, Inc., 1994.

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4

Toennies, Klaus D. Guide to Medical Image Analysis. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2751-2.

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5

Toennies, Klaus D. Guide to Medical Image Analysis. London: Springer London, 2017. http://dx.doi.org/10.1007/978-1-4471-7320-5.

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6

S, Awwal Abdul A., ed. Field guide to image processing. Bellingham, Wash: SPIE Press, 2012.

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7

P, Ford John, United States. National Aeronautics and Space Administration., and Jet Propulsion Laboratory (U.S.), eds. Guide to Magellan image interpretation. Pasadena, Calif: National Aeronautics and Space Administration, 1993.

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8

Bovik, Alan C. The essential guide to image processing. London: Academic Press, 2009.

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9

Becker, Carolyn Black. Reflections body image program: Facilitator guide. Oxford, NewYork: Oxford University Press, 2011.

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10

1964-, Willert Christian E., and Kompenhans Jürgen 1946-, eds. Particle image velocimetry: A practical guide. Berlin: Springer, 1998.

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Частини книг з теми "Image guide"

1

Weik, Martin H. "image guide." In Computer Science and Communications Dictionary, 751. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_8646.

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2

Beyerer, Jürgen, and Michael Heizmann. "Image evaluation and interpretation." In Technology Guide, 150–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-88546-7_29.

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3

Toennies, Klaus D. "Image Enhancement." In Guide to Medical Image Analysis, 125–72. London: Springer London, 2017. http://dx.doi.org/10.1007/978-1-4471-7320-5_4.

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4

Markelov, Andrey. "Image Management." In Certified OpenStack Administrator Study Guide, 31–42. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2125-9_4.

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5

Toennies, Klaus D. "Image Enhancement." In Guide to Medical Image Analysis, 111–46. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2751-2_4.

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6

Lee, Chen-Yu, Von-Wun Soo, and Yi-Ting Fu. "An Image Annotation Guide Agent." In Intelligent Agents and Multi-Agent Systems, 148–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-32128-6_12.

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7

Salomon, David. "Image Compression." In A Guide to Data Compression Methods, 81–166. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-0-387-21708-6_4.

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8

Meacham, Kenneth S. "Image Production." In The MRI Study Guide for Technologists, 39–62. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-2534-8_4.

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9

Burns, M. Shane. "Image Processing." In A Practical Guide to Observational Astronomy, 89–106. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003203919-5.

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10

Toennies, Klaus D. "Digital Image Acquisition." In Guide to Medical Image Analysis, 23–94. London: Springer London, 2017. http://dx.doi.org/10.1007/978-1-4471-7320-5_2.

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Тези доповідей конференцій з теми "Image guide"

1

Li, Qinghang, Lucia Zamorano, Zhaowei Jiang, Hans J. Holdener, Paul King, and Fernando Diaz. "Interactive Image Guided Surgery: The Experience of Wayne State University." In ASME 1997 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/imece1997-0323.

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Abstract Stereotactic localization using multimodality of medical images (such as CT or MRI) is increasingly employed to guide neurosurgical procedures 1,2,3,5. This technique provides highly accurate intraoperative localization and allows the surgeon to resect a lesion in its entirety while sparing critical areas of the brain. Image guided interactive frameless stereotaxy has been successfully applied to intracranial surgery and spine surgery. It has demonstrated a potential for reducing both operative time and morbidity. In this paper we described our interactive image guided surgery system and the experience of appling to different kind of surgery.
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2

Nassif, M., E. Dupont, H. Al Hajjar, and F. Lamarque. "Endoscopic additive manufacturing through image-guide *." In 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). IEEE, 2019. http://dx.doi.org/10.1109/aim.2019.8868505.

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3

Powell, John W. "Image hologram as visual light guide." In IS&T/SPIE 1994 International Symposium on Electronic Imaging: Science and Technology, edited by Stephen A. Benton. SPIE, 1994. http://dx.doi.org/10.1117/12.172629.

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4

Li, Yao, Hideo Kosaka, Ting Wang, Shigeru Kawai, and Kenichi Kasahara. "Applications of Fiber Image Guides to Bit-parallel Optical Interconnections." In Optical Computing. Washington, D.C.: Optica Publishing Group, 1995. http://dx.doi.org/10.1364/optcomp.1995.othb5.

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Анотація:
A fiber image guide, whether being a coherent fiber bundle or a single gradientindex fiber, has been known useful to transmit image signals. Such a fiber image guide has been successfully used in various medical endoscopic and industrial inspection applications [1,2]. High resolution analog images can be obtained for transmission distances ranging several meters or longer. Depending on used fiber materials, relative low loss transmission can be achieved at certain transmission wavelengths.
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5

Zhang, Jun, Peng Chuan Yang, Ze Bin Chen, Wen Long Hu, and Kun Wang. "Reliable Crack Depth Monitoring based on Guided Wave through Image Dielectric Guide." In 2021 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition (iWEM). IEEE, 2021. http://dx.doi.org/10.1109/iwem53379.2021.9790689.

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6

Aguerrebere, Cecilia, Mauricio Delbracio, Alberto Bartesaghi, and Guillermo Sapiro. "A Practical Guide to Multi-Image Alignment." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8461588.

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7

Fan, Zhiwen, Huafeng Wu, Xueyang Fu, Yue Huang, and Xinghao Ding. "Residual-Guide Network for Single Image Deraining." In MM '18: ACM Multimedia Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3240508.3240694.

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8

Luo, Simon, John Etgen, and Min Zhou. "Seismic adaptive optics with a guide image." In SEG Technical Program Expanded Abstracts 2017. Society of Exploration Geophysicists, 2017. http://dx.doi.org/10.1190/segam2017-17787151.1.

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9

Mei-Qi Shi and Ding-Hua Jiang. "Filled Image Guide for Millimeter-Wave Circuits." In MTT-S International Microwave Symposium Digest. MTT005, 1987. http://dx.doi.org/10.1109/mwsym.1987.1132484.

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10

M N, Chaitra, Khateeja Ambareen, and S. Meenakshi Sundaram. "Developers Guide for Android Applications." In 3rd National Conference on Image Processing, Computing, Communication, Networking and Data Analytics. AIJR Publisher, 2018. http://dx.doi.org/10.21467/proceedings.1.36.

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Звіти організацій з теми "Image guide"

1

Bigger, Rory, Benoît Blaysat, Christofer Boo, Manuel Grewer, Jun Hu, Amanda Jones, Markus Klein, et al. A Good Practices Guide for Digital Image Correlation. Edited by Elizabeth Jones and Mark Iadicola. International Digital Image Correlation Society, October 2018. http://dx.doi.org/10.32720/idics/gpg.ed1.

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2

Bigger, Rory, Benoît Blaysat, Christofer Boo, Manuel Grewer, Jun Hu, Amanda Jones, Markus Klein, et al. A Good Practices Guide for Digital Image Correlation. Edited by Elizabeth Jones and Mark Iadicola. International Digital Image Correlation Society, October 2018. http://dx.doi.org/10.32720/idics/gpg.ed1/print.format.

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3

Garris, Michael D., Craig I. Watson, R. Michael McCabe, and Charles L. Wilson. User's guide to NIST fingerprint image software (NFIS). Gaithersburg, MD: National Institute of Standards and Technology, 2001. http://dx.doi.org/10.6028/nist.ir.6813.

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4

Watson, Craig I., Michael D. Garris, Elham Tabassi, Charles L. Wilson, R. Michael McCabe, Stanley Janet, and Kenneth Ko. User's guide to NIST biometric image software (NBIS). Gaithersburg, MD: National Institute of Standards and Technology, 2007. http://dx.doi.org/10.6028/nist.ir.7392.

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5

Hahn, Charles D., Jerrell R. Ballard, and Jr. Image Data Acquisition System User's Guide and Technical Documentation. Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada399452.

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6

Max, C. E. ,. LLNL. Image improvement from a sodium-layer laser guide star adaptive optics system. Office of Scientific and Technical Information (OSTI), June 1997. http://dx.doi.org/10.2172/305943.

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7

Whitus, B. R., J. S. Goddard, W. B. Jatko, W. W. Manges, and D. A. Treece. Low-Speed Fingerprint Image Capture System User`s Guide, June 1, 1993. Office of Scientific and Technical Information (OSTI), June 1993. http://dx.doi.org/10.2172/10179252.

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8

Watson, Craig I., Michael D. Garris, Elham Tabassi, Charles L. Wilson, R. Michael McCabe, Stanley Janet, and Kenneth Ko. User's guide to export controlled distribution of NIST biometric image software (NBIS-EC). Gaithersburg, MD: National Institute of Standards and Technology, 2007. http://dx.doi.org/10.6028/nist.ir.7391.

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9

Adkins, Carl W. Users Guide for Fire Image Analysis System - Version 5.0: A Tool for Measuring Fire Behavior Characteristics. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southeastern Forest Experiment Station, 1995. http://dx.doi.org/10.2737/se-gtr-093.

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10

Adkins, Carl W. Users Guide for Fire Image Analysis System - Version 5.0: A Tool for Measuring Fire Behavior Characteristics. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southeastern Forest Experiment Station, 1995. http://dx.doi.org/10.2737/se-gtr-93.

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