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Artykuły w czasopismach na temat "Images 2D"
Jung, Sukwoo, Seunghyun Song, Minho Chang i Sangchul Park. "Range image registration based on 2D synthetic images". Computer-Aided Design 94 (styczeń 2018): 16–27. http://dx.doi.org/10.1016/j.cad.2017.08.001.
Pełny tekst źródłaTsalafoutas, Ioannis A., Angeliki C. Epistatou i 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.
Pełny tekst źródłaPlattard, Delphine, Marine Soret, Jocelyne Troccaz, Patrick Vassal, Jean-Yves Giraud, Guillaume Champleboux, Xavier Artignan i Michel Bolla. "Patient Set-Up Using Portal Images: 2D/2D Image Registration Using Mutual Information". Computer Aided Surgery 5, nr 4 (styczeń 2000): 246–62. http://dx.doi.org/10.3109/10929080009148893.
Pełny tekst źródłaKim, Jin-Mo, Jong-Yoon Kim i 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.
Pełny tekst źródłaJIANG, 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.
Pełny tekst źródłaKOVALEVSKY, VLADIMIR. "CURVATURE IN DIGITAL 2D IMAGES". International Journal of Pattern Recognition and Artificial Intelligence 15, nr 07 (listopad 2001): 1183–200. http://dx.doi.org/10.1142/s0218001401001283.
Pełny tekst źródłaKohnen, James B. "Images of Organization. 2d ed". Quality Management Journal 5, nr 2 (styczeń 1998): 117. http://dx.doi.org/10.1080/10686967.1998.11918859.
Pełny tekst źródłaKaczmarek, K., B. Walczak, S. de Jong i B. G. M. Vandeginste. "Matching 2D Gel Electrophoresis Images". Journal of Chemical Information and Computer Sciences 43, nr 3 (maj 2003): 978–86. http://dx.doi.org/10.1021/ci0256337.
Pełny tekst źródłaBae, Kitae, i 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.
Pełny tekst źródłaWang, Yong Sheng. "Fast 3D Human Face Modeling Method Based on Multiple View 2D Images". Applied Mechanics and Materials 273 (styczeń 2013): 796–99. http://dx.doi.org/10.4028/www.scientific.net/amm.273.796.
Pełny tekst źródłaRozprawy doktorskie na temat "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.
Pełny tekst źródłaJones, Jonathan-Lee. "2D and 3D segmentation of medical images". Thesis, Swansea University, 2015. https://cronfa.swan.ac.uk/Record/cronfa42504.
Pełny tekst źródłaGuarnera, Giuseppe Claudio. "Shape Modeling and Description from 2D Images". Doctoral thesis, Università di Catania, 2013. http://hdl.handle.net/10761/1365.
Pełny tekst źródłaSdiri, Bilel. "2D/3D Endoscopic image enhancement and analysis for video guided surgery". Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCD030.
Pełny tekst źródłaMinimally 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, i 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.
Pełny tekst źródłaHuang, Hui. "Efficient reconstruction of 2D images and 3D surfaces". Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2821.
Pełny tekst źródłaHenrichsen, Arne. "3D reconstruction and camera calibration from 2D images". Master's thesis, University of Cape Town, 2000. http://hdl.handle.net/11427/9725.
Pełny tekst źródłaA 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, i 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.
Pełny tekst źródłaPå 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.
Pełny tekst źródłaBowden, Nathan Charles. "Camera based texture mapping: 3D applications for 2D images". Texas A&M University, 2005. http://hdl.handle.net/1969.1/2407.
Pełny tekst źródłaKsiążki na temat "Images 2D"
Jones, Alun Gwyn. Recovering 3D shape from 2D images. Manchester: University of Manchester, 1995.
Znajdź pełny tekst źródłaEdexcel, red. Art and Design.GNVQ Intermediate.Unit 1:2D and 3D Visual Language.Student Preparatory Work (Pre-seen Images). January 2003. London: Edexcel, 2001.
Znajdź pełny tekst źródłaCappellini, Vito, red. Electronic Imaging & the Visual Arts. EVA 2013 Florence. Florence: Firenze University Press, 2013. http://dx.doi.org/10.36253/978-88-6655-372-4.
Pełny tekst źródłaCappellini, Vito, red. Electronic Imaging & the Visual Arts. EVA 2015 Florence. Florence: Firenze University Press, 2015. http://dx.doi.org/10.36253/978-88-6655-759-3.
Pełny tekst źródłaCappellini, Vito, red. Electronic Imaging & the Visual Arts. EVA 2014 Florence. Florence: Firenze University Press, 2014. http://dx.doi.org/10.36253/978-88-6655-573-5.
Pełny tekst źródłaFlusser, Jan, Tomáš Suk i Barbara Zitová. 2D and 3D Image Analysis by Moments. Chichester, UK: John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781119039402.
Pełny tekst źródłaWatson, Kenneth. A 2D FFT filtering program for image processing with examples. [Denver, CO]: U.S. Dept. of the Interior, Geological Survey, 1992.
Znajdź pełny tekst źródłaWosnitza, Matthias Werner. High precision 1024-point FFT processor for 2D object detection. Hartung-Gorre: Konstanz, 1999.
Znajdź pełny tekst źródłaKumar, Sandeep, Shilpa Rani i K. Ramya Laxmi. Artificial Intelligence and Machine Learning in 2D/3D Medical Image Processing. Redaktor Rohit Raja. First edition. | Boca Raton: CRC Press, 2021.: CRC Press, 2020. http://dx.doi.org/10.1201/9780429354526.
Pełny tekst źródłaJones, Angie. Thinking animation: Bridging the gap between 2D and CG. Boston, MA: Thomson Course Technology, 2007.
Znajdź pełny tekst źródłaCzęści książek na temat "Images 2D"
Arheit, Marcel, Daniel Castaño-Díez, Raphaël Thierry, Bryant R. Gipson, Xiangyan Zeng i Henning Stahlberg. "Image Processing of 2D Crystal Images". W Methods in Molecular Biology, 171–94. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-176-9_10.
Pełny tekst źródłaNakanishi, Tomoko M. "3D Images". W Novel Plant Imaging and Analysis, 191–96. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4992-6_6.
Pełny tekst źródłaKimmel, Ron. "2D and 3D Image Segmentation". W 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.
Pełny tekst źródłaKovalevsky, Vladimir. "Edge Detection in 2D Images". W Image Processing with Cellular Topology, 113–38. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5772-6_6.
Pełny tekst źródłaMuresan, Lucian. "2D → 2D geometric transformation invariant to arbitrary translations, rotations and scales". W 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.
Pełny tekst źródłaLiu, Chuan, Jiaqi Shen, Yue Ren i Hao Zheng. "Pipes of AI – Machine Learning Assisted 3D Modeling Design". W Proceedings of the 2020 DigitalFUTURES, 17–26. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4400-6_2.
Pełny tekst źródłaTakayama, Natsuki, Shubing Meng i Takahashi Hiroki. "Choshi Design System from 2D Images". W 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.
Pełny tekst źródłaBruijn, Norbert P., i Fiona M. Clements. "Quantitative Analysis of 2D Echocardiography Images". W Transesophageal Echocardiography, 85–118. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-2025-8_5.
Pełny tekst źródłaYang, Chuan-Kai, i Chia-Ning Kuo. "Automatically Extracting Hairstyles from 2D Images". W Advances in Visual Computing, 406–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41939-3_39.
Pełny tekst źródłaKorres, Georgios, i Mohamad Eid. "Touching 2D Images Using Haptogram System". W Lecture Notes in Electrical Engineering, 71–73. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4157-0_12.
Pełny tekst źródłaStreszczenia konferencji na temat "Images 2D"
Brown, Michael S., i W. Brent Seales. "Beyond 2D images". W the fifth ACM conference. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/336597.336623.
Pełny tekst źródłaSikander Hayat Khiyal, Malik, Aihab Khan i Amna Bibi. "Modified Watershed Algorithm for Segmentation of 2D Images". W InSITE 2009: Informing Science + IT Education Conference. Informing Science Institute, 2009. http://dx.doi.org/10.28945/3349.
Pełny tekst źródłaS, Vaishnavi A., i Sumana M. "Evolution of 3D images from 2D images". W 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2021. http://dx.doi.org/10.1109/conecct52877.2021.9622698.
Pełny tekst źródłaRasool, Shahzad, i Alexei Sourin. "Haptic interaction with 2D images". W the 10th International Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2087756.2087758.
Pełny tekst źródłaErturk, Rumeysa Ashhan, i Mustafa Ersel Karnasak. "Anthropometric Measurements with 2D Images". W 2022 7th International Conference on Computer Science and Engineering (UBMK). IEEE, 2022. http://dx.doi.org/10.1109/ubmk55850.2022.9919504.
Pełny tekst źródłaSabharwal, Chaman L. "Recovering 3D image parameters from corresponding two 2D images". W the 1993 ACM/SIGAPP symposium. New York, New York, USA: ACM Press, 1993. http://dx.doi.org/10.1145/162754.162948.
Pełny tekst źródłaSantos Ferrer, Juan C., David González Chévere i Vidya Manian. "Photorealistic image synthesis and camera validation from 2D images". W SPIE Sensing Technology + Applications, redaktorzy Bahram Javidi, Jung-Young Son, Osamu Matoba, Manuel Martínez-Corral i Adrian Stern. SPIE, 2014. http://dx.doi.org/10.1117/12.2050916.
Pełny tekst źródłaSon, Minjung, Henry Kang, Yunjin Lee i Seungyong Lee. "Abstract Line Drawings from 2D Images". W 15th Pacific Conference on Computer Graphics and Applications (PG'07). IEEE, 2007. http://dx.doi.org/10.1109/pg.2007.63.
Pełny tekst źródłaWidanagamaachchi, W. N., i A. T. Dharmaratne. "3D Face Reconstruction from 2D Images". W 2008 Digital Image Computing: Techniques and Applications. IEEE, 2008. http://dx.doi.org/10.1109/dicta.2008.83.
Pełny tekst źródłaLi Yuan i Zhi-chun Mu. "Ear Recognition based on 2D Images". W 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems. IEEE, 2007. http://dx.doi.org/10.1109/btas.2007.4401941.
Pełny tekst źródłaRaporty organizacyjne na temat "Images 2D"
Basri, Ronen, i Daphna Weinshall. Distance Metric between 3D Models and 2D Images for Recognition and Classification. Fort Belvoir, VA: Defense Technical Information Center, lipiec 1992. http://dx.doi.org/10.21236/ada260069.
Pełny tekst źródłaHanson, Allen R. Recognizing 3 D Objects from 2D Images Using Structural Knowledge Base of Genetic Views. Fort Belvoir, VA: Defense Technical Information Center, sierpień 1988. http://dx.doi.org/10.21236/ada207875.
Pełny tekst źródłaArroyo, Marcos, Riccardo Rorato, Marco Previtali i Matteo Ciantia. 2D Image-based calibration of rolling resistance in 3D discrete element models of sand. University of Dundee, grudzień 2021. http://dx.doi.org/10.20933/100001229.
Pełny tekst źródłaRiedel, Michael, Jörg Bialas, Elisa Klein, Cord Papenberg i 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.
Pełny tekst źródłaDecroux, Agnes, Kassem Kalo i 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), marzec 2021. http://dx.doi.org/10.55274/r0012068.
Pełny tekst źródłaMidak, Liliia Ya, Ivan V. Kravets, Olga V. Kuzyshyn, Khrystyna V. Berladyniuk, Khrystyna V. Buzhdyhan, Liliia V. Baziuk i Aleksandr D. Uchitel. Augmented reality in process of studying astronomic concepts in primary school. [б. в.], listopad 2020. http://dx.doi.org/10.31812/123456789/4411.
Pełny tekst źródłaCreighton, 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, lipiec 2020. http://dx.doi.org/10.14509/30533.
Pełny tekst źródłaWEHLBURG, JOSEPH C., CHRISTINE M. WEHLBURG, JODY L. SMITH, OLGA B. SPAHN, MARK W. SMITH i CRAIG M. BONEY. High Speed 2D Hadamard Transform Spectral Imager. Office of Scientific and Technical Information (OSTI), luty 2003. http://dx.doi.org/10.2172/808596.
Pełny tekst źródłaAnderson, Gerald L., i Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, grudzień 2002. http://dx.doi.org/10.32747/2002.7585193.bard.
Pełny tekst źródłaHabib, Ayman, Darcy M. Bullock, Yi-Chun Lin i Raja Manish. Road Ditch Line Mapping with Mobile LiDAR. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317354.
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