Auswahl der wissenschaftlichen Literatur zum Thema „Images 2D“
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Zeitschriftenartikel zum Thema "Images 2D"
Jung, Sukwoo, Seunghyun Song, Minho Chang und Sangchul Park. „Range image registration based on 2D synthetic images“. Computer-Aided Design 94 (Januar 2018): 16–27. http://dx.doi.org/10.1016/j.cad.2017.08.001.
Der volle Inhalt der QuelleTsalafoutas, Ioannis A., Angeliki C. Epistatou und Konstantinos K. Delibasis. „Image Quality Comparison between Digital Breast Tomosynthesis Images and 2D Mammographic Images Using the CDMAM Test Object“. Journal of Imaging 8, Nr. 8 (21.08.2022): 223. http://dx.doi.org/10.3390/jimaging8080223.
Der volle Inhalt der QuellePlattard, Delphine, Marine Soret, Jocelyne Troccaz, Patrick Vassal, Jean-Yves Giraud, Guillaume Champleboux, Xavier Artignan und Michel Bolla. „Patient Set-Up Using Portal Images: 2D/2D Image Registration Using Mutual Information“. Computer Aided Surgery 5, Nr. 4 (Januar 2000): 246–62. http://dx.doi.org/10.3109/10929080009148893.
Der volle Inhalt der QuelleKim, Jin-Mo, Jong-Yoon Kim und Hyung-Je Cho. „Warping of 2D Facial Images Using Image Interpolation by Triangle Subdivision“. Journal of Korea Game Society 14, Nr. 2 (20.04.2014): 55–66. http://dx.doi.org/10.7583/jkgs.2014.14.2.55.
Der volle Inhalt der QuelleJIANG, C. F. „3D IMAGE RECONSTRUCTION OF OVARIAN TUMOR IN THE ULTRASONIC IMAGES“. Biomedical Engineering: Applications, Basis and Communications 13, Nr. 02 (25.04.2001): 93–98. http://dx.doi.org/10.4015/s1016237201000121.
Der volle Inhalt der QuelleKOVALEVSKY, VLADIMIR. „CURVATURE IN DIGITAL 2D IMAGES“. International Journal of Pattern Recognition and Artificial Intelligence 15, Nr. 07 (November 2001): 1183–200. http://dx.doi.org/10.1142/s0218001401001283.
Der volle Inhalt der QuelleKohnen, James B. „Images of Organization. 2d ed“. Quality Management Journal 5, Nr. 2 (Januar 1998): 117. http://dx.doi.org/10.1080/10686967.1998.11918859.
Der volle Inhalt der QuelleKaczmarek, K., B. Walczak, S. de Jong und B. G. M. Vandeginste. „Matching 2D Gel Electrophoresis Images“. Journal of Chemical Information and Computer Sciences 43, Nr. 3 (Mai 2003): 978–86. http://dx.doi.org/10.1021/ci0256337.
Der volle Inhalt der QuelleBae, Kitae, und Hyoungjin Kim. „Optimal Point Correspondence for Image Registration in 2D Images“. International Journal of Multimedia and Ubiquitous Engineering 8, Nr. 6 (30.11.2013): 127–40. http://dx.doi.org/10.14257/ijmue.2013.8.6.13.
Der volle Inhalt der QuelleWang, Yong Sheng. „Fast 3D Human Face Modeling Method Based on Multiple View 2D Images“. Applied Mechanics and Materials 273 (Januar 2013): 796–99. http://dx.doi.org/10.4028/www.scientific.net/amm.273.796.
Der volle Inhalt der QuelleDissertationen zum Thema "Images 2D"
Truong, Michael Vi Nguyen. „2D-3D registration of cardiac images“. Thesis, King's College London (University of London), 2014. https://kclpure.kcl.ac.uk/portal/en/theses/2d3d-registration-of-cardiac-images(afef93e6-228c-4bc7-aab0-94f1e1ecf006).html.
Der volle Inhalt der QuelleJones, Jonathan-Lee. „2D and 3D segmentation of medical images“. Thesis, Swansea University, 2015. https://cronfa.swan.ac.uk/Record/cronfa42504.
Der volle Inhalt der QuelleGuarnera, Giuseppe Claudio. „Shape Modeling and Description from 2D Images“. Doctoral thesis, Università di Catania, 2013. http://hdl.handle.net/10761/1365.
Der volle Inhalt der QuelleSdiri, Bilel. „2D/3D Endoscopic image enhancement and analysis for video guided surgery“. Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCD030.
Der volle Inhalt der QuelleMinimally invasive surgery has made remarkable progress in the last decades and became a very popular diagnosis and treatment tool, especially with the rapid medical and technological advances leading to innovative new tools such as robotic surgical systems and wireless capsule endoscopy. Due to the intrinsic characteristics of the endoscopic environment including dynamic illumination conditions and moist tissues with high reflectance, endoscopic images suffer often from several degradations such as large dark regions,with low contrast and sharpness, and many artifacts such as specular reflections and blur. These challenges together with the introduction of three dimensional(3D) imaging surgical systems have prompted the question of endoscopic images quality, which needs to be enhanced. The latter process aims either to provide the surgeons/doctors with a better visual feedback or improve the outcomes of some subsequent tasks such as features extraction for 3D organ reconstruction and registration. This thesis addresses the problem of endoscopic image quality enhancement by proposing novel enhancement techniques for both two-dimensional (2D) and stereo (i.e. 3D)endoscopic images.In the context of automatic tissue abnormality detection and classification for gastro-intestinal tract disease diagnosis, we proposed a pre-processing enhancement method for 2D endoscopic images and wireless capsule endoscopy improving both local and global contrast. The proposed method expose inner subtle structures and tissues details, which improves the features detection process and the automatic classification rate of neoplastic,non-neoplastic and inflammatory tissues. Inspired by binocular vision attention features of the human visual system, we proposed in another workan adaptive enhancement technique for stereo endoscopic images combining depth and edginess information. The adaptability of the proposed method consists in adjusting the enhancement to both local image activity and depth level within the scene while controlling the interview difference using abinocular perception model. A subjective experiment was conducted to evaluate the performance of the proposed algorithm in terms of visual qualityby both expert and non-expert observers whose scores demonstrated the efficiency of our 3D contrast enhancement technique. In the same scope, we resort in another recent stereo endoscopic image enhancement work to the wavelet domain to target the enhancement towards specific image components using the multiscale representation and the efficient space-frequency localization property. The proposed joint enhancement methods rely on cross-view processing and depth information, for both the wavelet decomposition and the enhancement steps, to exploit the inter-view redundancies together with perceptual human visual system properties related to contrast sensitivity and binocular combination and rivalry. The visual qualityof the processed images and objective assessment metrics demonstrate the efficiency of our joint stereo enhancement in adjusting the image illuminationin both dark and saturated regions and emphasizing local image details such as fine veins and micro vessels, compared to other endoscopic enhancement techniques for 2D and 3D images
Meng, Ting, und Yating Yu. „Deconvolution algorithms of 2D Transmission Electron Microscopy images“. Thesis, KTH, Optimeringslära och systemteori, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-110096.
Der volle Inhalt der QuelleHuang, Hui. „Efficient reconstruction of 2D images and 3D surfaces“. Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2821.
Der volle Inhalt der QuelleHenrichsen, Arne. „3D reconstruction and camera calibration from 2D images“. Master's thesis, University of Cape Town, 2000. http://hdl.handle.net/11427/9725.
Der volle Inhalt der QuelleA 3D reconstruction technique from stereo images is presented that needs minimal intervention from the user. The reconstruction problem consists of three steps, each of which is equivalent to the estimation of a specific geometry group. The first step is the estimation of the epipolar geometry that exists between the stereo image pair, a process involving feature matching in both images. The second step estimates the affine geometry, a process of finding a special plane in projective space by means of vanishing points. Camera calibration forms part of the third step in obtaining the metric geometry, from which it is possible to obtain a 3D model of the scene. The advantage of this system is that the stereo images do not need to be calibrated in order to obtain a reconstruction. Results for both the camera calibration and reconstruction are presented to verify that it is possible to obtain a 3D model directly from features in the images.
Agerskov, Niels, und Gabriel Carrizo. „Application for Deriving 2D Images from 3D CT Image Data for Research Purposes“. Thesis, KTH, Skolan för teknik och hälsa (STH), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-190881.
Der volle Inhalt der QuellePå Karolinska universitetssjukhuset, Huddinge har man länge önskat möjligheten att utföra mallningar av höftproteser med hjälp av data från datortomografiundersökningar (DT). Detta har hittills inte varit möjligt eftersom programmet som används för mallning av höftproteser enbart accepterar traditionella slätröntgenbilder. Därför var syftet med detta projekt att skapa en mjukvaru-applikation som kan användas för att generera 2D-bilder för mallning av proteser från DT-data. För att skapa applikationen användes huvudsakligen Python-kodbiblioteken NumPy och The Visualization Toolkit (VTK) tillsammans med användargränssnittsbiblioteket PyQt4. I applikationen ingår ett grafiskt användargränssnitt och metoder för optimering av bilderna i mallningssammanhang. Applikationen fungerar men bildernas kvalitet måste utvärderas med en större urvalsgrupp.
Srinivasan, Nirmala. „Cross-Correlation Of Biomedical Images Using Two Dimensional Discrete Hermite Functions“. University of Akron / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=akron1341866987.
Der volle Inhalt der QuelleBowden, Nathan Charles. „Camera based texture mapping: 3D applications for 2D images“. Texas A&M University, 2005. http://hdl.handle.net/1969.1/2407.
Der volle Inhalt der QuelleBücher zum Thema "Images 2D"
Jones, Alun Gwyn. Recovering 3D shape from 2D images. Manchester: University of Manchester, 1995.
Den vollen Inhalt der Quelle findenEdexcel, Hrsg. Art and Design.GNVQ Intermediate.Unit 1:2D and 3D Visual Language.Student Preparatory Work (Pre-seen Images). January 2003. London: Edexcel, 2001.
Den vollen Inhalt der Quelle findenCappellini, Vito, Hrsg. Electronic Imaging & the Visual Arts. EVA 2013 Florence. Florence: Firenze University Press, 2013. http://dx.doi.org/10.36253/978-88-6655-372-4.
Der volle Inhalt der QuelleCappellini, Vito, Hrsg. Electronic Imaging & the Visual Arts. EVA 2015 Florence. Florence: Firenze University Press, 2015. http://dx.doi.org/10.36253/978-88-6655-759-3.
Der volle Inhalt der QuelleCappellini, Vito, Hrsg. Electronic Imaging & the Visual Arts. EVA 2014 Florence. Florence: Firenze University Press, 2014. http://dx.doi.org/10.36253/978-88-6655-573-5.
Der volle Inhalt der QuelleFlusser, Jan, Tomáš Suk und Barbara Zitová. 2D and 3D Image Analysis by Moments. Chichester, UK: John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781119039402.
Der volle Inhalt der QuelleWatson, Kenneth. A 2D FFT filtering program for image processing with examples. [Denver, CO]: U.S. Dept. of the Interior, Geological Survey, 1992.
Den vollen Inhalt der Quelle findenWosnitza, Matthias Werner. High precision 1024-point FFT processor for 2D object detection. Hartung-Gorre: Konstanz, 1999.
Den vollen Inhalt der Quelle findenKumar, Sandeep, Shilpa Rani und K. Ramya Laxmi. Artificial Intelligence and Machine Learning in 2D/3D Medical Image Processing. Herausgegeben von Rohit Raja. First edition. | Boca Raton: CRC Press, 2021.: CRC Press, 2020. http://dx.doi.org/10.1201/9780429354526.
Der volle Inhalt der QuelleJones, Angie. Thinking animation: Bridging the gap between 2D and CG. Boston, MA: Thomson Course Technology, 2007.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Images 2D"
Arheit, Marcel, Daniel Castaño-Díez, Raphaël Thierry, Bryant R. Gipson, Xiangyan Zeng und Henning Stahlberg. „Image Processing of 2D Crystal Images“. In Methods in Molecular Biology, 171–94. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-176-9_10.
Der volle Inhalt der QuelleNakanishi, Tomoko M. „3D Images“. In Novel Plant Imaging and Analysis, 191–96. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4992-6_6.
Der volle Inhalt der QuelleKimmel, Ron. „2D and 3D Image Segmentation“. In Numerical Geometry of Images, 123–40. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-0-387-21637-9_9.
Der volle Inhalt der QuelleKovalevsky, Vladimir. „Edge Detection in 2D Images“. In Image Processing with Cellular Topology, 113–38. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5772-6_6.
Der volle Inhalt der QuelleMuresan, Lucian. „2D → 2D geometric transformation invariant to arbitrary translations, rotations and scales“. In Computer Analysis of Images and Patterns, 90–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63460-6_104.
Der volle Inhalt der QuelleLiu, Chuan, Jiaqi Shen, Yue Ren und Hao Zheng. „Pipes of AI – Machine Learning Assisted 3D Modeling Design“. In Proceedings of the 2020 DigitalFUTURES, 17–26. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4400-6_2.
Der volle Inhalt der QuelleTakayama, Natsuki, Shubing Meng und Takahashi Hiroki. „Choshi Design System from 2D Images“. In Lecture Notes in Computer Science, 358–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15399-0_38.
Der volle Inhalt der QuelleBruijn, Norbert P., und Fiona M. Clements. „Quantitative Analysis of 2D Echocardiography Images“. In Transesophageal Echocardiography, 85–118. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-2025-8_5.
Der volle Inhalt der QuelleYang, Chuan-Kai, und Chia-Ning Kuo. „Automatically Extracting Hairstyles from 2D Images“. In Advances in Visual Computing, 406–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41939-3_39.
Der volle Inhalt der QuelleKorres, Georgios, und Mohamad Eid. „Touching 2D Images Using Haptogram System“. In Lecture Notes in Electrical Engineering, 71–73. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4157-0_12.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Images 2D"
Brown, Michael S., und W. Brent Seales. „Beyond 2D images“. In the fifth ACM conference. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/336597.336623.
Der volle Inhalt der QuelleSikander Hayat Khiyal, Malik, Aihab Khan und Amna Bibi. „Modified Watershed Algorithm for Segmentation of 2D Images“. In InSITE 2009: Informing Science + IT Education Conference. Informing Science Institute, 2009. http://dx.doi.org/10.28945/3349.
Der volle Inhalt der QuelleS, Vaishnavi A., und Sumana M. „Evolution of 3D images from 2D images“. In 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2021. http://dx.doi.org/10.1109/conecct52877.2021.9622698.
Der volle Inhalt der QuelleRasool, Shahzad, und Alexei Sourin. „Haptic interaction with 2D images“. In the 10th International Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2087756.2087758.
Der volle Inhalt der QuelleErturk, Rumeysa Ashhan, und Mustafa Ersel Karnasak. „Anthropometric Measurements with 2D Images“. In 2022 7th International Conference on Computer Science and Engineering (UBMK). IEEE, 2022. http://dx.doi.org/10.1109/ubmk55850.2022.9919504.
Der volle Inhalt der QuelleSabharwal, Chaman L. „Recovering 3D image parameters from corresponding two 2D images“. In the 1993 ACM/SIGAPP symposium. New York, New York, USA: ACM Press, 1993. http://dx.doi.org/10.1145/162754.162948.
Der volle Inhalt der QuelleSantos Ferrer, Juan C., David González Chévere und Vidya Manian. „Photorealistic image synthesis and camera validation from 2D images“. In SPIE Sensing Technology + Applications, herausgegeben von Bahram Javidi, Jung-Young Son, Osamu Matoba, Manuel Martínez-Corral und Adrian Stern. SPIE, 2014. http://dx.doi.org/10.1117/12.2050916.
Der volle Inhalt der QuelleSon, Minjung, Henry Kang, Yunjin Lee und Seungyong Lee. „Abstract Line Drawings from 2D Images“. In 15th Pacific Conference on Computer Graphics and Applications (PG'07). IEEE, 2007. http://dx.doi.org/10.1109/pg.2007.63.
Der volle Inhalt der QuelleWidanagamaachchi, W. N., und A. T. Dharmaratne. „3D Face Reconstruction from 2D Images“. In 2008 Digital Image Computing: Techniques and Applications. IEEE, 2008. http://dx.doi.org/10.1109/dicta.2008.83.
Der volle Inhalt der QuelleLi Yuan und Zhi-chun Mu. „Ear Recognition based on 2D Images“. In 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems. IEEE, 2007. http://dx.doi.org/10.1109/btas.2007.4401941.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Images 2D"
Basri, Ronen, und Daphna Weinshall. Distance Metric between 3D Models and 2D Images for Recognition and Classification. Fort Belvoir, VA: Defense Technical Information Center, Juli 1992. http://dx.doi.org/10.21236/ada260069.
Der volle Inhalt der QuelleHanson, Allen R. Recognizing 3 D Objects from 2D Images Using Structural Knowledge Base of Genetic Views. Fort Belvoir, VA: Defense Technical Information Center, August 1988. http://dx.doi.org/10.21236/ada207875.
Der volle Inhalt der QuelleArroyo, Marcos, Riccardo Rorato, Marco Previtali und Matteo Ciantia. 2D Image-based calibration of rolling resistance in 3D discrete element models of sand. University of Dundee, Dezember 2021. http://dx.doi.org/10.20933/100001229.
Der volle Inhalt der QuelleRiedel, Michael, Jörg Bialas, Elisa Klein, Cord Papenberg und Janine Berndt. Technical Report for Raw 2D MCS Reflection Data, R/V Sonne Cruise 294, Vancouver (Canada) – Port Hueneme (USA), 13/09/22 – 27/10/23. GEOMAR Helmholtz Centre for Ocean Research Kiel, Germany, 2024. http://dx.doi.org/10.3289/tr_2d-mcs_so294.
Der volle Inhalt der QuelleDecroux, Agnes, Kassem Kalo und Keith Swinden. PR-393-205100-R01 IRIS X-Ray CT Qualification for Flexible Pipe Inspection (Phase 1). Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), März 2021. http://dx.doi.org/10.55274/r0012068.
Der volle Inhalt der QuelleMidak, Liliia Ya, Ivan V. Kravets, Olga V. Kuzyshyn, Khrystyna V. Berladyniuk, Khrystyna V. Buzhdyhan, Liliia V. Baziuk und Aleksandr D. Uchitel. Augmented reality in process of studying astronomic concepts in primary school. [б. в.], November 2020. http://dx.doi.org/10.31812/123456789/4411.
Der volle Inhalt der QuelleCreighton, Dan. X-ray diffraction, x-ray fluorescence, pyrolysis, source rock analysis, 2D SEM images from cuttings for wells: Malguk 1, Merak 1, Pipeline State 1, West Kavik 1, and Wolfbutton 25-6-9. Alaska Division of Geological & Geophysical Surveys, Juli 2020. http://dx.doi.org/10.14509/30533.
Der volle Inhalt der QuelleWEHLBURG, JOSEPH C., CHRISTINE M. WEHLBURG, JODY L. SMITH, OLGA B. SPAHN, MARK W. SMITH und CRAIG M. BONEY. High Speed 2D Hadamard Transform Spectral Imager. Office of Scientific and Technical Information (OSTI), Februar 2003. http://dx.doi.org/10.2172/808596.
Der volle Inhalt der QuelleAnderson, Gerald L., und Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, Dezember 2002. http://dx.doi.org/10.32747/2002.7585193.bard.
Der volle Inhalt der QuelleHabib, Ayman, Darcy M. Bullock, Yi-Chun Lin und Raja Manish. Road Ditch Line Mapping with Mobile LiDAR. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317354.
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