Literatura científica selecionada sobre o tema "Images 2D"
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Artigos de revistas sobre o assunto "Images 2D"
Jung, Sukwoo, Seunghyun Song, Minho Chang e Sangchul Park. "Range image registration based on 2D synthetic images". Computer-Aided Design 94 (janeiro de 2018): 16–27. http://dx.doi.org/10.1016/j.cad.2017.08.001.
Texto completo da fonteTsalafoutas, Ioannis A., Angeliki C. Epistatou e Konstantinos K. Delibasis. "Image Quality Comparison between Digital Breast Tomosynthesis Images and 2D Mammographic Images Using the CDMAM Test Object". Journal of Imaging 8, n.º 8 (21 de agosto de 2022): 223. http://dx.doi.org/10.3390/jimaging8080223.
Texto completo da fontePlattard, Delphine, Marine Soret, Jocelyne Troccaz, Patrick Vassal, Jean-Yves Giraud, Guillaume Champleboux, Xavier Artignan e Michel Bolla. "Patient Set-Up Using Portal Images: 2D/2D Image Registration Using Mutual Information". Computer Aided Surgery 5, n.º 4 (janeiro de 2000): 246–62. http://dx.doi.org/10.3109/10929080009148893.
Texto completo da fonteKim, Jin-Mo, Jong-Yoon Kim e Hyung-Je Cho. "Warping of 2D Facial Images Using Image Interpolation by Triangle Subdivision". Journal of Korea Game Society 14, n.º 2 (20 de abril de 2014): 55–66. http://dx.doi.org/10.7583/jkgs.2014.14.2.55.
Texto completo da fonteJIANG, C. F. "3D IMAGE RECONSTRUCTION OF OVARIAN TUMOR IN THE ULTRASONIC IMAGES". Biomedical Engineering: Applications, Basis and Communications 13, n.º 02 (25 de abril de 2001): 93–98. http://dx.doi.org/10.4015/s1016237201000121.
Texto completo da fonteKOVALEVSKY, VLADIMIR. "CURVATURE IN DIGITAL 2D IMAGES". International Journal of Pattern Recognition and Artificial Intelligence 15, n.º 07 (novembro de 2001): 1183–200. http://dx.doi.org/10.1142/s0218001401001283.
Texto completo da fonteKohnen, James B. "Images of Organization. 2d ed". Quality Management Journal 5, n.º 2 (janeiro de 1998): 117. http://dx.doi.org/10.1080/10686967.1998.11918859.
Texto completo da fonteKaczmarek, K., B. Walczak, S. de Jong e B. G. M. Vandeginste. "Matching 2D Gel Electrophoresis Images". Journal of Chemical Information and Computer Sciences 43, n.º 3 (maio de 2003): 978–86. http://dx.doi.org/10.1021/ci0256337.
Texto completo da fonteBae, Kitae, e Hyoungjin Kim. "Optimal Point Correspondence for Image Registration in 2D Images". International Journal of Multimedia and Ubiquitous Engineering 8, n.º 6 (30 de novembro de 2013): 127–40. http://dx.doi.org/10.14257/ijmue.2013.8.6.13.
Texto completo da fonteWang, Yong Sheng. "Fast 3D Human Face Modeling Method Based on Multiple View 2D Images". Applied Mechanics and Materials 273 (janeiro de 2013): 796–99. http://dx.doi.org/10.4028/www.scientific.net/amm.273.796.
Texto completo da fonteTeses / dissertações sobre o assunto "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.
Texto completo da fonteJones, Jonathan-Lee. "2D and 3D segmentation of medical images". Thesis, Swansea University, 2015. https://cronfa.swan.ac.uk/Record/cronfa42504.
Texto completo da fonteGuarnera, Giuseppe Claudio. "Shape Modeling and Description from 2D Images". Doctoral thesis, Università di Catania, 2013. http://hdl.handle.net/10761/1365.
Texto completo da fonteSdiri, Bilel. "2D/3D Endoscopic image enhancement and analysis for video guided surgery". Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCD030.
Texto completo da fonteMinimally 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, e 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.
Texto completo da fonteHuang, Hui. "Efficient reconstruction of 2D images and 3D surfaces". Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2821.
Texto completo da fonteHenrichsen, Arne. "3D reconstruction and camera calibration from 2D images". Master's thesis, University of Cape Town, 2000. http://hdl.handle.net/11427/9725.
Texto completo da fonteA 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, e 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.
Texto completo da fontePå 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.
Texto completo da fonteBowden, Nathan Charles. "Camera based texture mapping: 3D applications for 2D images". Texas A&M University, 2005. http://hdl.handle.net/1969.1/2407.
Texto completo da fonteLivros sobre o assunto "Images 2D"
Jones, Alun Gwyn. Recovering 3D shape from 2D images. Manchester: University of Manchester, 1995.
Encontre o texto completo da fonteEdexcel, ed. Art and Design.GNVQ Intermediate.Unit 1:2D and 3D Visual Language.Student Preparatory Work (Pre-seen Images). January 2003. London: Edexcel, 2001.
Encontre o texto completo da fonteCappellini, Vito, ed. Electronic Imaging & the Visual Arts. EVA 2013 Florence. Florence: Firenze University Press, 2013. http://dx.doi.org/10.36253/978-88-6655-372-4.
Texto completo da fonteCappellini, Vito, ed. Electronic Imaging & the Visual Arts. EVA 2015 Florence. Florence: Firenze University Press, 2015. http://dx.doi.org/10.36253/978-88-6655-759-3.
Texto completo da fonteCappellini, Vito, ed. Electronic Imaging & the Visual Arts. EVA 2014 Florence. Florence: Firenze University Press, 2014. http://dx.doi.org/10.36253/978-88-6655-573-5.
Texto completo da fonteFlusser, Jan, Tomáš Suk e Barbara Zitová. 2D and 3D Image Analysis by Moments. Chichester, UK: John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781119039402.
Texto completo da fonteWatson, Kenneth. A 2D FFT filtering program for image processing with examples. [Denver, CO]: U.S. Dept. of the Interior, Geological Survey, 1992.
Encontre o texto completo da fonteWosnitza, Matthias Werner. High precision 1024-point FFT processor for 2D object detection. Hartung-Gorre: Konstanz, 1999.
Encontre o texto completo da fonteKumar, Sandeep, Shilpa Rani e K. Ramya Laxmi. Artificial Intelligence and Machine Learning in 2D/3D Medical Image Processing. Editado por Rohit Raja. First edition. | Boca Raton: CRC Press, 2021.: CRC Press, 2020. http://dx.doi.org/10.1201/9780429354526.
Texto completo da fonteJones, Angie. Thinking animation: Bridging the gap between 2D and CG. Boston, MA: Thomson Course Technology, 2007.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Images 2D"
Arheit, Marcel, Daniel Castaño-Díez, Raphaël Thierry, Bryant R. Gipson, Xiangyan Zeng e 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.
Texto completo da fonteNakanishi, 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.
Texto completo da fonteKimmel, 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.
Texto completo da fonteKovalevsky, 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.
Texto completo da fonteMuresan, 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.
Texto completo da fonteLiu, Chuan, Jiaqi Shen, Yue Ren e 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.
Texto completo da fonteTakayama, Natsuki, Shubing Meng e 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.
Texto completo da fonteBruijn, Norbert P., e 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.
Texto completo da fonteYang, Chuan-Kai, e 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.
Texto completo da fonteKorres, Georgios, e 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.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Images 2D"
Brown, Michael S., e 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.
Texto completo da fonteSikander Hayat Khiyal, Malik, Aihab Khan e 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.
Texto completo da fonteS, Vaishnavi A., e 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.
Texto completo da fonteRasool, Shahzad, e 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.
Texto completo da fonteErturk, Rumeysa Ashhan, e 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.
Texto completo da fonteSabharwal, 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.
Texto completo da fonteSantos Ferrer, Juan C., David González Chévere e Vidya Manian. "Photorealistic image synthesis and camera validation from 2D images". In SPIE Sensing Technology + Applications, editado por Bahram Javidi, Jung-Young Son, Osamu Matoba, Manuel Martínez-Corral e Adrian Stern. SPIE, 2014. http://dx.doi.org/10.1117/12.2050916.
Texto completo da fonteSon, Minjung, Henry Kang, Yunjin Lee e 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.
Texto completo da fonteWidanagamaachchi, W. N., e 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.
Texto completo da fonteLi Yuan e 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.
Texto completo da fonteRelatórios de organizações sobre o assunto "Images 2D"
Basri, Ronen, e Daphna Weinshall. Distance Metric between 3D Models and 2D Images for Recognition and Classification. Fort Belvoir, VA: Defense Technical Information Center, julho de 1992. http://dx.doi.org/10.21236/ada260069.
Texto completo da fonteHanson, Allen R. Recognizing 3 D Objects from 2D Images Using Structural Knowledge Base of Genetic Views. Fort Belvoir, VA: Defense Technical Information Center, agosto de 1988. http://dx.doi.org/10.21236/ada207875.
Texto completo da fonteArroyo, Marcos, Riccardo Rorato, Marco Previtali e Matteo Ciantia. 2D Image-based calibration of rolling resistance in 3D discrete element models of sand. University of Dundee, dezembro de 2021. http://dx.doi.org/10.20933/100001229.
Texto completo da fonteRiedel, Michael, Jörg Bialas, Elisa Klein, Cord Papenberg e 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.
Texto completo da fonteDecroux, Agnes, Kassem Kalo e 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), março de 2021. http://dx.doi.org/10.55274/r0012068.
Texto completo da fonteMidak, Liliia Ya, Ivan V. Kravets, Olga V. Kuzyshyn, Khrystyna V. Berladyniuk, Khrystyna V. Buzhdyhan, Liliia V. Baziuk e Aleksandr D. Uchitel. Augmented reality in process of studying astronomic concepts in primary school. [б. в.], novembro de 2020. http://dx.doi.org/10.31812/123456789/4411.
Texto completo da fonteCreighton, 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, julho de 2020. http://dx.doi.org/10.14509/30533.
Texto completo da fonteWEHLBURG, JOSEPH C., CHRISTINE M. WEHLBURG, JODY L. SMITH, OLGA B. SPAHN, MARK W. SMITH e CRAIG M. BONEY. High Speed 2D Hadamard Transform Spectral Imager. Office of Scientific and Technical Information (OSTI), fevereiro de 2003. http://dx.doi.org/10.2172/808596.
Texto completo da fonteAnderson, Gerald L., e Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, dezembro de 2002. http://dx.doi.org/10.32747/2002.7585193.bard.
Texto completo da fonteHabib, Ayman, Darcy M. Bullock, Yi-Chun Lin e Raja Manish. Road Ditch Line Mapping with Mobile LiDAR. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317354.
Texto completo da fonte