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Статті в журналах з теми "Deep learning segmentation"
Noori, Amani Y., Dr Shaimaa H. Shaker, and Dr Raghad Abdulaali Azeez. "Semantic Segmentation of Urban Street Scenes Using Deep Learning." Webology 19, no. 1 (January 20, 2022): 2294–306. http://dx.doi.org/10.14704/web/v19i1/web19156.
Повний текст джерелаAL-Oudat, Mohammad, Mohammad Azzeh, Hazem Qattous, Ahmad Altamimi, and Saleh Alomari. "Image Segmentation based Deep Learning for Biliary Tree Diagnosis." Webology 19, no. 1 (January 20, 2022): 1834–49. http://dx.doi.org/10.14704/web/v19i1/web19123.
Повний текст джерелаSri, S. Vinitha, and S. P. Kavya. "Lung Segmentation Using Deep Learning." Asian Journal of Applied Science and Technology 05, no. 02 (2021): 10–19. http://dx.doi.org/10.38177/ajast.2021.5202.
Повний текст джерелаVogt, Nina. "Neuron segmentation with deep learning." Nature Methods 16, no. 6 (May 30, 2019): 460. http://dx.doi.org/10.1038/s41592-019-0450-7.
Повний текст джерелаHyun-Cheol Park, Hyun-Cheol Park, Raman Ghimire Hyun-Cheol Park, Sahadev Poudel Raman Ghimire, and Sang-Woong Lee Sahadev Poudel. "Deep Learning for Joint Classification and Segmentation of Histopathology Image." 網際網路技術學刊 23, no. 4 (July 2022): 903–10. http://dx.doi.org/10.53106/160792642022072304025.
Повний текст джерелаWeishaupt, L. L., T. Vuong, A. Thibodeau-Antonacci, A. Garant, K. S. Singh, C. Miller, A. Martin, and S. Enger. "A121 QUANTIFYING INTER-OBSERVER VARIABILITY IN THE SEGMENTATION OF RECTAL TUMORS IN ENDOSCOPY IMAGES AND ITS EFFECTS ON DEEP LEARNING." Journal of the Canadian Association of Gastroenterology 5, Supplement_1 (February 21, 2022): 140–42. http://dx.doi.org/10.1093/jcag/gwab049.120.
Повний текст джерелаIwaszenko, Sebastian, and Leokadia Róg. "Application of Deep Learning in Petrographic Coal Images Segmentation." Minerals 11, no. 11 (November 13, 2021): 1265. http://dx.doi.org/10.3390/min11111265.
Повний текст джерелаYang, Zi, Mingli Chen, Mahdieh Kazemimoghadam, Lin Ma, Strahinja Stojadinovic, Robert Timmerman, Tu Dan, Zabi Wardak, Weiguo Lu, and Xuejun Gu. "Deep-learning and radiomics ensemble classifier for false positive reduction in brain metastases segmentation." Physics in Medicine & Biology 67, no. 2 (January 19, 2022): 025004. http://dx.doi.org/10.1088/1361-6560/ac4667.
Повний текст джерелаXue, Jie, Bao Wang, Yang Ming, Xuejun Liu, Zekun Jiang, Chengwei Wang, Xiyu Liu, et al. "Deep learning–based detection and segmentation-assisted management of brain metastases." Neuro-Oncology 22, no. 4 (December 23, 2019): 505–14. http://dx.doi.org/10.1093/neuonc/noz234.
Повний текст джерелаNapte, Kiran, and Anurag Mahajan. "Deep Learning based Liver Segmentation: A Review." Revue d'Intelligence Artificielle 36, no. 6 (December 31, 2022): 979–84. http://dx.doi.org/10.18280/ria.360620.
Повний текст джерелаДисертації з теми "Deep learning segmentation"
Favia, Federico. "Real-time hand segmentation using deep learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292930.
Повний текст джерелаHandsegmentering är en grundläggande del av många datorvisionssystem som syftar till gestigenkänning eller handspårning. I synnerhet behöver förstärkta verklighetslösningar ett mycket exakt gestanalyssystem för att tillfredsställa slutkonsumenterna på ett lämpligt sätt. Därför är handsegmenteringssteget kritiskt. Segmentering är ett välkänt problem vid bildbehandling, det vill säga processen att dela en digital bild i flera regioner med pixlar av liknande kvaliteter. Klassificera vilka pixlar som tillhör handen och vilka som hör till bakgrunden måste utföras i realtidsprestanda och rimlig beräkningskomplexitet. Medan tidigare använts huvudsakligen lättviktiga probabilistiska metoder och maskininlärningsmetoder, undersöker detta arbete utmaningarna med realtidshandsegmentering uppnådd genom flera djupinlärningstekniker. Är det möjligt eller inte att förbättra nuvarande toppmoderna segmenteringssystem för smartphone-applikationer? Flera modeller testas och jämförs baserat på noggrannhet och processhastighet. Transfer learning-liknande metoden leder metoden för detta arbete eftersom många arkitekturer byggdes bara för generisk semantisk segmentering eller för specifika applikationer som autonom körning. Stora ansträngningar läggs på att organisera en gedigen och generaliserad uppsättning händer, utnyttja befintliga och data som samlats in av ManoMotion AB. Eftersom det första syftet var att få en riktigt exakt handsegmentering, väljs i slutändan RefineNetarkitekturen och både kvantitativa och kvalitativa utvärderingar utförs med beaktande av fördelarna med det och analys av problemen relaterade till beräkningstiden som kan förbättras i framtiden.
Sarpangala, Kishan. "Semantic Segmentation Using Deep Learning Neural Architectures." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin157106185092304.
Повний текст джерелаWen, Shuangyue. "Automatic Tongue Contour Segmentation using Deep Learning." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38343.
Повний текст джерела¿, Ananya. "DEEP LEARNING METHODS FOR CROP AND WEED SEGMENTATION." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1528372119706623.
Повний текст джерелаTosteberg, Patrik. "Semantic Segmentation of Point Clouds Using Deep Learning." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-136793.
Повний текст джерелаKolhatkar, Dhanvin. "Real-Time Instance and Semantic Segmentation Using Deep Learning." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/40616.
Повний текст джерелаWang, Wei. "Image Segmentation Using Deep Learning Regulated by Shape Context." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-227261.
Повний текст джерелаUnder de senaste åren har bildsegmentering med hjälp av djupa neurala nätverk gjort stora framsteg. Att nå ett bra resultat med träning med en liten mängd data kvarstår emellertid som en utmaning. För att hitta ett bra sätt att förbättra noggrannheten i segmenteringen med begränsade datamängder så implementerade vi en ny segmentering för automatiska röntgenbilder av bröstkorgsdiagram baserat på tidigare forskning av Chunliang. Detta tillvägagångssätt använder djupt lärande neurala nätverk kombinerat med "shape context" information. I detta experiment skapade vi en ny nätverkstruktur genom omkonfiguration av U-nätverket till en 2-inputstruktur och förfinade pipeline processeringssteget där bilden och "shape contexten" var tränade tillsammans genom den nya nätverksmodellen genom iteration.Den föreslagna metoden utvärderades på dataset med 247 bröströntgenfotografier, och n-faldig korsvalidering användes för utvärdering. Resultatet visar att den föreslagna pipelinen jämfört med ursprungs U-nätverket når högre noggrannhet när de tränas med begränsade datamängder. De "begränsade" dataseten här hänvisar till 1-20 bilder inom det medicinska fältet. Ett bättre resultat med högre noggrannhet kan nås om den andra strukturen förfinas ytterligare och "shape context-generatorns" parameter finjusteras.
Chen, Yani. "Deep Learning based 3D Image Segmentation Methods and Applications." Ohio University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1547066297047003.
Повний текст джерелаLiu, Dongnan. "Supervised and Unsupervised Deep Learning-based Biomedical Image Segmentation." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/24744.
Повний текст джерелаGranli, Petter. "Semantic segmentation of seabed sonar imagery using deep learning." Thesis, Linköpings universitet, Programvara och system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160561.
Повний текст джерелаКниги з теми "Deep learning segmentation"
Wang, Xiaogang. Deep Learning in Object Recognition, Detection, and Segmentation. Now Publishers, 2016.
Знайти повний текст джерелаBrain Tumor MRI Image Segmentation Using Deep Learning Techniques. Elsevier, 2022. http://dx.doi.org/10.1016/c2021-0-00056-0.
Повний текст джерелаChaki, Jyotismita. Brain Tumor MRI Image Segmentation Using Deep Learning Techniques. Elsevier Science & Technology, 2021.
Знайти повний текст джерелаChaki, Jyotismita. Brain Tumor MRI Image Segmentation Using Deep Learning Techniques. Elsevier Science & Technology Books, 2021.
Знайти повний текст джерелаAdvanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, Deep RL, Unsupervised Learning, Object Detection and Segmentation, and More, 2nd Edition. Packt Publishing, Limited, 2020.
Знайти повний текст джерелаЧастини книг з теми "Deep learning segmentation"
Hatamizadeh, Ali, Assaf Hoogi, Debleena Sengupta, Wuyue Lu, Brian Wilcox, Daniel Rubin, and Demetri Terzopoulos. "Deep Active Lesion Segmentation." In Machine Learning in Medical Imaging, 98–105. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32692-0_12.
Повний текст джерелаBenoit, Alexandre, Badih Ghattas, Emna Amri, Joris Fournel, and Patrick Lambert. "Deep Learning for Semantic Segmentation." In Multi-faceted Deep Learning, 39–72. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74478-6_3.
Повний текст джерелаMoskalenko, Viktor, Nikolai Zolotykh, and Grigory Osipov. "Deep Learning for ECG Segmentation." In Studies in Computational Intelligence, 246–54. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30425-6_29.
Повний текст джерелаYang, Hao-Yu. "Deep Learning in Brain Segmentation." In Handbook of Artificial Intelligence in Biomedical Engineering, 261–88. Series statement: Biomedical engineering: techniques and applications: Apple Academic Press, 2020. http://dx.doi.org/10.1201/9781003045564-12.
Повний текст джерелаKaur, Prabhjot, and Anand Muni Mishra. "Segmentation of Deep Learning Models." In Machine Learning for Edge Computing, 115–26. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003143468-8.
Повний текст джерелаKeydana, Sigrid. "Image Segmentation." In Deep Learning and Scientific Computing with R torch, 181–200. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003275923-19.
Повний текст джерелаEzeobiejesi, Jude, and Bir Bhanu. "Latent Fingerprint Image Segmentation Using Deep Neural Network." In Deep Learning for Biometrics, 83–107. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61657-5_4.
Повний текст джерелаJalilian, Ehsaneddin, and Andreas Uhl. "Iris Segmentation Using Fully Convolutional Encoder–Decoder Networks." In Deep Learning for Biometrics, 133–55. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61657-5_6.
Повний текст джерелаRashed, Hazem, Senthil Yogamani, Ahmad El-Sallab, Mohamed Elhelw, and Mahmoud Hassaballah. "Deep Semantic Segmentation in Autonomous Driving." In Deep Learning in Computer Vision, 151–82. First edition. | Boca Raton, FL : CRC Press/Taylor and Francis, 2020. |: CRC Press, 2020. http://dx.doi.org/10.1201/9781351003827-6.
Повний текст джерелаMunir, Khushboo, Fabrizio Frezza, and Antonello Rizzi. "Deep Learning for Brain Tumor Segmentation." In Studies in Computational Intelligence, 189–201. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6321-8_11.
Повний текст джерелаТези доповідей конференцій з теми "Deep learning segmentation"
Kamkova, Yuliia, Hemin Ali Qadir, Ole Jakob, and Rahul Prasanna Kumar. "Kidney and tumor segmentation using combined Deep learning method." In 2019 Kidney Tumor Segmentation Challenge: KiTS19. University of Minnesota Libraries Publishing, 2019. http://dx.doi.org/10.24926/548719.091.
Повний текст джерелаRaein Hashemi, Seyed, Boris Gershman, and Vladimir I. Valtchinov. "Development of a Deep Learning Algorithm for Segmentation of Kidney Tumor Imaging." In 2019 Kidney Tumor Segmentation Challenge: KiTS19. University of Minnesota Libraries Publishing, 2019. http://dx.doi.org/10.24926/548719.083.
Повний текст джерелаSouza, Alan, Wilson Leao, Daniel Miranda, Nelson Hargreaves, Bruno Dias, and Erick Talarico. "Salt segmentation using deep learning." In International Congress of the Brazilian Geophysical Society&Expogef. Brazilian Geophysical Society, 2019. http://dx.doi.org/10.22564/16cisbgf2019.219.
Повний текст джерелаZhou, Xueting, Yan Chen, and Shoushan Liu. "Deep learning for image segmentation." In ICAIP 2022: 2022 6th International Conference on Advances in Image Processing. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3577117.3577144.
Повний текст джерелаJunior, Gerivan, Janderson Ferreira, Cristian Millan-Aria, Ramiro Daniel, Alberto Casado, and Bruno Fernandes. "Ceramic Cracks Segmentation with Deep Learning." In LatinX in AI at International Conference on Machine Learning 2021. Journal of LatinX in AI Research, 2021. http://dx.doi.org/10.52591/202107245.
Повний текст джерелаJunior, Gerivan, Janderson Ferreira, Cristian Millan-Aria, Ramiro Daniel, Alberto Casado, and Bruno Fernandes. "Ceramic Cracks Segmentation with Deep Learning." In LatinX in AI at International Conference on Machine Learning 2021. Journal of LatinX in AI Research, 2021. http://dx.doi.org/10.52591/lxai202107245.
Повний текст джерелаMüller, Dominik, and Frank Kramer. "MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning." In 2019 Kidney Tumor Segmentation Challenge: KiTS19. University of Minnesota Libraries Publishing, 2019. http://dx.doi.org/10.24926/548719.074.
Повний текст джерелаMehrubeoglu, Mehrube, Isaac Vargas, Chi Huang, and Kirk Cammarata. "Segmentation of seagrass blade images using deep learning." In Real-Time Image Processing and Deep Learning 2021, edited by Nasser Kehtarnavaz and Matthias F. Carlsohn. SPIE, 2021. http://dx.doi.org/10.1117/12.2587057.
Повний текст джерелаVijay, Amishi, Jasleen Saini, and B. S. Saini. "A Review of Brain Tumor Image Segmentation of MR Images Using Deep Learning Methods." In International Conference on Women Researchers in Electronics and Computing. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.114.19.
Повний текст джерелаLiu, Yun, Peng-Tao Jiang, Vahan Petrosyan, Shi-Jie Li, Jiawang Bian, Le Zhang, and Ming-Ming Cheng. "DEL: Deep Embedding Learning for Efficient Image Segmentation." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/120.
Повний текст джерелаЗвіти організацій з теми "Deep learning segmentation"
Chang, Ke-Vin. Deep Learning Algorithm for Automatic Localization and Segmentation of the Median Nerve: a Protocol for Systematic Review and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, May 2022. http://dx.doi.org/10.37766/inplasy2022.5.0074.
Повний текст джерелаHuang, Haohang, Erol Tutumluer, Jiayi Luo, Kelin Ding, Issam Qamhia, and John Hart. 3D Image Analysis Using Deep Learning for Size and Shape Characterization of Stockpile Riprap Aggregates—Phase 2. Illinois Center for Transportation, September 2022. http://dx.doi.org/10.36501/0197-9191/22-017.
Повний текст джерелаAlhasson, Haifa F., and Shuaa S. Alharbi. New Trends in image-based Diabetic Foot Ucler Diagnosis Using Machine Learning Approaches: A Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2022. http://dx.doi.org/10.37766/inplasy2022.11.0128.
Повний текст джерелаPatwa, B., P. L. St-Charles, G. Bellefleur, and B. Rousseau. Predictive models for first arrivals on seismic reflection data, Manitoba, New Brunswick, and Ontario. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329758.
Повний текст джерела