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Статті в журналах з теми "U-NET CNN"
Sariturk, Batuhan, and Dursun Zafer Seker. "A Residual-Inception U-Net (RIU-Net) Approach and Comparisons with U-Shaped CNN and Transformer Models for Building Segmentation from High-Resolution Satellite Images." Sensors 22, no. 19 (October 8, 2022): 7624. http://dx.doi.org/10.3390/s22197624.
Повний текст джерелаChoi, Keong-Hun, and Jong-Eun Ha. "Edge Detection based-on U-Net using Edge Classification CNN." Journal of Institute of Control, Robotics and Systems 25, no. 8 (August 31, 2019): 684–89. http://dx.doi.org/10.5302/j.icros.2019.19.0119.
Повний текст джерелаDi Benedetto, Alessandro, Margherita Fiani, and Lucas Matias Gujski. "U-Net-Based CNN Architecture for Road Crack Segmentation." Infrastructures 8, no. 5 (May 6, 2023): 90. http://dx.doi.org/10.3390/infrastructures8050090.
Повний текст джерелаDjohar, Muhammad Awaludin, Anita Desiani, Dewi Lestari Dwi Putri, Des Alwine Zayanti, Ali Amran, Irmeilyana Irmeilyana, and Novi Rustiana Dewi. "Segmentasi Citra Hati Menggunakan Metode Convolutional Neural Network dengan Arsitektur U-Net." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 6, no. 1 (July 23, 2022): 221–34. http://dx.doi.org/10.31289/jite.v6i1.6751.
Повний текст джерелаMiron, Casian, Laura Ioana Grigoras, Radu Ciucu, and Vasile Manta. "Eye Image Segmentation Method Based on the Modified U-Net CNN Architecture." Bulletin of the Polytechnic Institute of Iași. Electrical Engineering, Power Engineering, Electronics Section 67, no. 2 (June 1, 2021): 41–52. http://dx.doi.org/10.2478/bipie-2021-0010.
Повний текст джерелаSariturk, Batuhan, Damla Kumbasar, and Dursun Zafer Seker. "Comparative Analysis of Different CNN Models for Building Segmentation from Satellite and UAV Images." Photogrammetric Engineering & Remote Sensing 89, no. 2 (February 1, 2023): 97–105. http://dx.doi.org/10.14358/pers.22-00084r2.
Повний текст джерелаErdem, Firat, Nuri Erkin Ocer, Dilek Kucuk Matci, Gordana Kaplan, and Ugur Avdan. "Apricot Tree Detection from UAV-Images Using Mask R-CNN and U-Net." Photogrammetric Engineering & Remote Sensing 89, no. 2 (February 1, 2023): 89–96. http://dx.doi.org/10.14358/pers.22-00086r2.
Повний текст джерелаK.Narasimha Rao, Kesani Prudhvidhar Reddy, Gopavarapu Sai Satya Sreekar, and Gade Gopinath Reddy. "Retinal blood vessels segmentation using CNN algorithm." international journal of engineering technology and management sciences 7, no. 3 (2023): 499–504. http://dx.doi.org/10.46647/ijetms.2023.v07i03.70.
Повний текст джерелаLutsenko, V. S., and A. E. Shukhman. "SEGMENTATION OF MEDICAL IMAGES BY CONVOLUTIONAL NEURAL NETWORKS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 216 (June 2022): 40–50. http://dx.doi.org/10.14489/vkit.2022.06.pp.040-050.
Повний текст джерелаYounisse, Remah, Rawan Ghnemat, and Jaafer Al Saraireh. "Fine-tuning U-net for medical image segmentation based on activation function, optimizer and pooling layer." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 5 (October 1, 2023): 5406. http://dx.doi.org/10.11591/ijece.v13i5.pp5406-5417.
Повний текст джерелаДисертації з теми "U-NET CNN"
Scotti, Alessandro. "Sviluppo e validazione di un nuovo approccio basato su reti neurali convoluzionali 3D per la valutazione della progressione della malattia policistica renale autosomica dominante." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
Знайти повний текст джерелаHellgren, Robin, and Martin Axelsson. "An evaluation of using a U-Net CNN with a random forest pre-screener : On a dataset of hand-drawn maps provided by länsstyrelsen i Jönköping." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20003.
Повний текст джерелаVincenzi, Fabian. "Reti neurali convoluzionali per il miglioramento di immagini tomografiche ad angoli limitati." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/22199/.
Повний текст джерелаBerezina, Polina. "Enhancing Hurricane Damage Assessment from Satellite Images Using Deep Learning." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587554383454681.
Повний текст джерелаDíaz, Pinto Andrés Yesid. "Machine Learning for Glaucoma Assessment using Fundus Images." Doctoral thesis, Universitat Politècnica de València, 2019. http://hdl.handle.net/10251/124351.
Повний текст джерела[CAT] Les imatges de fons d'ull són molt utilitzades pels oftalmòlegs per a l'avaluació de la retina i la detecció de glaucoma. Aquesta patologia és la segona causa de ceguesa al món, segons estudis de l'Organització Mundial de la Salut (OMS). En aquesta tesi doctoral, s'estudien algoritmes d'aprenentatge automàtic (machine learning) per a l'avaluació automàtica del glaucoma usant imatges de fons d'ull. En primer lloc, es proposen dos mètodes per a la segmentació automàtica. El primer mètode utilitza la transformació Watershed Estocàstica per segmentar la copa òptica i després mesurar característiques clíniques com la relació Copa / Disc i la regla ISNT. El segon mètode és una arquitectura U-Net que s'usa específicament per a la segmentació del disc òptic i la copa òptica. A continuació, es presenten sistemes automàtics d'avaluació del glaucoma basats en xarxes neuronals convolucionals (CNN per les sigles en anglès). En aquest enfocament s'utilitzen diferents models entrenats en ImageNet com classificadors automàtics de glaucoma, usant fine-tuning. Aquesta nova tècnica permet detectar el glaucoma sense segmentació prèvia o extracció de característiques. A més, aquest enfocament presenta una millora considerable del rendiment comparat amb altres treballs de l'estat de l'art. En tercer lloc, donada la dificultat d'obtenir grans quantitats d'imatges etiquetades (glaucoma / no glaucoma), aquesta tesi també aborda el problema de la síntesi d'imatges de la retina. En concret es van analitzar dues arquitectures diferents per a la síntesi d'imatges, les arquitectures Variational Autoencoder (VAE) i la Generative adversarial Networks (GAN). Amb aquestes arquitectures es van generar imatges sintètiques que es van analitzar qualitativament i quantitativament, obtenint un rendiment similar a altres treballs a la literatura. Finalment, en aquesta tesi es planteja la utilització d'un tipus de GAN (DCGAN) com a alternativa als sistemes automàtics d'avaluació del glaucoma presentats anteriorment. Per assolir aquest objectiu es va implementar un algoritme d'aprenentatge semi-supervisat.
[EN] Fundus images are widely used by ophthalmologists to assess the retina and detect glaucoma, which is, according to studies from the World Health Organization (WHO), the second cause of blindness worldwide. In this thesis, machine learning algorithms for automatic glaucoma assessment using fundus images are studied. First, two methods for automatic segmentation are proposed. The first method uses the Stochastic Watershed transformation to segment the optic cup and measures clinical features such as the Cup/Disc ratio and ISNT rule. The second method is a U-Net architecture focused on the optic disc and optic cup segmentation task. Secondly, automated glaucoma assessment systems using convolutional neural networks (CNNs) are presented. In this approach, different ImageNet-trained models are fine-tuned and used as automatic glaucoma classifiers. These new techniques allow detecting glaucoma without previous segmentation or feature extraction. Moreover, it improves the performance of other state-of-art works. Thirdly, given the difficulty of getting large amounts of glaucoma-labelled images, this thesis addresses the problem of retinal image synthesis. Two different architectures for image synthesis, the Variational Autoencoder (VAE) and Generative Adversarial Networks (GAN) architectures, were analysed. Using these models, synthetic images that were qualitative and quantitative analysed, reporting state-of-the-art performance, were generated. Finally, an adversarial model is used to create an alternative automatic glaucoma assessment system. In this part, a semi-supervised learning algorithm was implemented to reach this goal.
The research derived from this doctoral thesis has been supported by the Generalitat Valenciana under the scholarship Santiago Grisolía [GRISOLIA/2015/027].
Díaz Pinto, AY. (2019). Machine Learning for Glaucoma Assessment using Fundus Images [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/124351
TESIS
Sousa, Joana Vale Amaro de. "Lung Segmentation in CT Images: A CNN U-Net hybrid approach on a cross-cohort dataset." Master's thesis, 2021. https://hdl.handle.net/10216/137790.
Повний текст джерелаКниги з теми "U-NET CNN"
Tse, Peter U. Two Types of Libertarian Free Will Are Realized in the Human Brain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190460723.003.0010.
Повний текст джерелаBiałowąs, Sylwester, ed. Experimental design and biometric research. Toward innovations. Wydawnictwo Uniwersytetu Ekonomicznego w Poznaniu, 2021. http://dx.doi.org/10.18559/978-83-8211-079-1.
Повний текст джерелаSkupio, Rafał. Zastosowanie nieinwazyjnych pomiarów rdzeni wiertniczych do zwiększenia informacji na temat parametrów skał zbiornikowych. Instytut Nafty i Gazu - Państwowy Instytut Badawczy, 2022. http://dx.doi.org/10.18668/pn2022.237.
Повний текст джерелаLos ancestrales juegos y deportes de pelota maya en Mesoamérica contemporánea. Universidad Libre sede principal, 2022. http://dx.doi.org/10.18041/978-628-7580-08-4.
Повний текст джерелаЧастини книг з теми "U-NET CNN"
Kumaravelan, Umashankar, and M. Nivedita. "Localized Super Resolution for Foreground Images Using U-Net and MR-CNN." In Lecture Notes in Electrical Engineering, 25–39. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7169-3_3.
Повний текст джерелаSharma, Utkarsh, Nimish Nigam, Ujjawal Kumar, Vinay Kumar, Sadanand Yadav, Ashish Pandey, and Rakesh Kumar Singh. "Abnormality Detection in Heart Using Combination of CNN, RNN and U-Net." In VLSI, Communication and Signal Processing, 135–46. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0973-5_10.
Повний текст джерелаKonopczyński, Tomasz, Ron Heiman, Piotr Woźnicki, Paweł Gniewek, Marie-Cécilia Duvernoy, Oskar Hallatschek, and Jürgen Hesser. "Instance Segmentation of Densely Packed Cells Using a Hybrid Model of U-Net and Mask R-CNN." In Artificial Intelligence and Soft Computing, 626–35. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61401-0_58.
Повний текст джерелаMaqsood, Sarmad, Robertas Damasevicius, and Faisal Mehmood Shah. "An Efficient Approach for the Detection of Brain Tumor Using Fuzzy Logic and U-NET CNN Classification." In Computational Science and Its Applications – ICCSA 2021, 105–18. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86976-2_8.
Повний текст джерелаChelle-Michou, Cyril, and Urs Schaltegger. "U–Pb Dating of Mineral Deposits: From Age Constraints to Ore-Forming Processes." In Isotopes in Economic Geology, Metallogenesis and Exploration, 37–87. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27897-6_3.
Повний текст джерелаZhang, Jindan, Jun Cai, Ying Su, Qingyou He, and Xinyue Lin. "Research and Development and Pilot Application of Innovative Technology of Prefabricated Concrete." In Lecture Notes in Civil Engineering, 226–37. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1260-3_20.
Повний текст джерелаHogan, Ciarán, and Ganesh Sistu. "Automatic Vehicle Ego Body Extraction for Reducing False Detections in Automated Driving Applications." In Communications in Computer and Information Science, 264–75. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26438-2_21.
Повний текст джерелаSikdar, Debosmita, Ivy Kanungo, and Dipanwita Das. "Microbial Enzymes: A Summary Focusing on Biotechnology Prospective for Combating Industrial Pollutants." In Proceedings of the Conference BioSangam 2022: Emerging Trends in Biotechnology (BIOSANGAM 2022), 70–76. Dordrecht: Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-020-6_8.
Повний текст джерелаYildirim, Kemal, Sami Al-Nawaiseh, Sophia Ehlers, Lukas Schießer, Michael Storck, Tobias Brix, Nicole Eter, and Julian Varghese. "U-Net-Based Segmentation of Current Imaging Biomarkers in OCT-Scans of Patients with Age Related Macular Degeneration." In Caring is Sharing – Exploiting the Value in Data for Health and Innovation. IOS Press, 2023. http://dx.doi.org/10.3233/shti230315.
Повний текст джерелаIslam, Mohammad Tariqul, Ferdaus Ahmed, Mowafa Househ, and Tanvir Alam. "Optical Disc Segmentation from Retinal Fundus Images Using Deep Learning." In Studies in Health Technology and Informatics. IOS Press, 2023. http://dx.doi.org/10.3233/shti230576.
Повний текст джерелаТези доповідей конференцій з теми "U-NET CNN"
Zhang, Chengzhu, and Yuxiang Xing. "CT artifact reduction via U-net CNN." In Image Processing, edited by Elsa D. Angelini and Bennett A. Landman. SPIE, 2018. http://dx.doi.org/10.1117/12.2293903.
Повний текст джерелаGhanshala, Anshul, Aakarshan Chauhan, Manoj Diwakar, and Sachin Sharma. "Brain Tumor Detection Using U-Net and 3D CNN Architecture." In 2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). IEEE, 2022. http://dx.doi.org/10.1109/icccis56430.2022.10037660.
Повний текст джерелаLiu, Yang, and Wei Yang. "Automatic liver segmentation using U-net in the assistance of CNN." In 2020 International Conference on Intelligent Computing, Automation and Systems (ICICAS). IEEE, 2020. http://dx.doi.org/10.1109/icicas51530.2020.00083.
Повний текст джерелаSuman, Abdulla Al, Yash Khemchandani, Md Asikuzzaman, Alexandra Louise Webb, Diana M. Perriman, Murat Tahtali, and Mark R. Pickering. "Evaluation of U-Net CNN Approaches for Human Neck MRI Segmentation." In 2020 Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2020. http://dx.doi.org/10.1109/dicta51227.2020.9363385.
Повний текст джерелаHsu, Aaron W., and Rodrigo Girão Serrão. "U-Net CNN in APL: Exploring Zero-Framework, Zero-Library Machine Learning." In ARRAY '23: 9th ACM SIGPLAN International Workshop on Libraries, Languages and Compilers for Array Programming. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3589246.3595371.
Повний текст джерелаSushma, B., C. K. Raghavendra, and J. Prashanth. "CNN based U-Net with Modified Skip Connections for Colon Polyp Segmentation." In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2021. http://dx.doi.org/10.1109/iccmc51019.2021.9418037.
Повний текст джерелаLi, Yuqin, Zhengang Jiang, Ke Zhang, Weili Shi, Fei He, and Jianhua Liu. "Dense-U-Net: A novel densely connected CNN for lung fields segmentation." In 2020 International Conference on Virtual Reality and Visualization (ICVRV). IEEE, 2020. http://dx.doi.org/10.1109/icvrv51359.2020.00035.
Повний текст джерелаNour, Abdala, Sherif Saad, and Boubakeur Boufama. "Prostate biomedical images segmentation and classification by using U-NET CNN model." In BCB '21: 12th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3459930.3471169.
Повний текст джерелаWang, Yinglong, and Lyu Zhou. "A Lung Nodule Detector Based on U-Net and 3D-CNN Model." In 2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI). IEEE, 2021. http://dx.doi.org/10.1109/cei52496.2021.9574604.
Повний текст джерелаHAN, GUILAI, WEI LIU, BENGUO YU, XIAOLING LI, LU LIU, and HAIXIA LI. "The Detection and Recognition of Pulmonary Nodules Based on U-net and CNN." In CSAI 2020: 2020 4th International Conference on Computer Science and Artificial Intelligence. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3445815.3445838.
Повний текст джерелаЗвіти організацій з теми "U-NET CNN"
Mevarech, Moshe, Jeremy Bruenn, and Yigal Koltin. Virus Encoded Toxin of the Corn Smut Ustilago Maydis - Isolation of Receptors and Mapping Functional Domains. United States Department of Agriculture, September 1995. http://dx.doi.org/10.32747/1995.7613022.bard.
Повний текст джерелаDownard, Alicia, Stephen Semmens, and Bryant Robbins. Automated characterization of ridge-swale patterns along the Mississippi River. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40439.
Повний текст джерелаReine, Kevin. A literature review of beach nourishment impacts on marine turtles. Engineer Research and Development Center (U.S.), March 2022. http://dx.doi.org/10.21079/11681/43829.
Повний текст джерелаPulugurtha, Srinivas S., Abimbola Ogungbire, and Chirag Akbari. Modeling and Evaluating Alternatives to Enhance Access to an Airport and Meet Future Expansion Needs. Mineta Transportation Institute, April 2023. http://dx.doi.org/10.31979/mti.2023.2120.
Повний текст джерелаTaylor. L51755 Development and Testing of an Advanced Technology Vibration Transmission. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), July 1996. http://dx.doi.org/10.55274/r0010124.
Повний текст джерела