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Artykuły w czasopismach na temat "Automatic threshold"
Waller, James K. "Intelligent automatic threshold circuit". Journal of the Acoustical Society of America 96, nr 1 (lipiec 1994): 616. http://dx.doi.org/10.1121/1.410401.
Pełny tekst źródłaZhao, Shuang Ping, Xiang Wei Li, Jing Hong Xing i Gong Zheng. "An Wavelet Image Automatic Threshold Selection Denoising Method". Advanced Materials Research 482-484 (luty 2012): 780–83. http://dx.doi.org/10.4028/www.scientific.net/amr.482-484.780.
Pełny tekst źródłaMidena, E., P. P. Radin, E. Convento i F. Cavarzeran. "Macular Automatic Fundus Perimetry Threshold versus Standard Perimetry Threshold". European Journal of Ophthalmology 17, nr 1 (styczeń 2007): 63–68. http://dx.doi.org/10.1177/112067210701700109.
Pełny tekst źródłaAdali, Tulay. "Automatic threshold selection using histogram quantization". Journal of Biomedical Optics 2, nr 2 (1.04.1997): 211. http://dx.doi.org/10.1117/12.268965.
Pełny tekst źródłaLopes, N. V., P. A. Mogadouro do Couto, H. Bustince i P. Melo-Pinto. "Automatic Histogram Threshold Using Fuzzy Measures". IEEE Transactions on Image Processing 19, nr 1 (styczeń 2010): 199–204. http://dx.doi.org/10.1109/tip.2009.2032349.
Pełny tekst źródłaWan, Yan, Li Yao i Bugao Xu. "Automatic Segmentation of Fiber Cross Sections by Dual Thresholding". Journal of Engineered Fibers and Fabrics 7, nr 1 (marzec 2012): 155892501200700. http://dx.doi.org/10.1177/155892501200700113.
Pełny tekst źródłaKANATANI, KENICHI, i YASUSHI KANAZAWA. "AUTOMATIC THRESHOLDING FOR CORRESPONDENCE DETECTION". International Journal of Image and Graphics 04, nr 01 (styczeń 2004): 21–33. http://dx.doi.org/10.1142/s0219467804001270.
Pełny tekst źródłaQiu, Y., A. R. Whittaker, M. Lucas i K. Anderson. "Automatic wheeze detection based on auditory modelling". Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 219, nr 3 (1.03.2005): 219–27. http://dx.doi.org/10.1243/095441105x28551.
Pełny tekst źródłaBardy, Fabrice, Bram Van Dun, Harvey Dillon, Mark Seeto, Humphry Qin, Teck Loi i Robert Cowan. "The Cortical Automatic Threshold Estimation in Adults". Hearing Journal 69, nr 6 (czerwiec 2016): 32. http://dx.doi.org/10.1097/01.hj.0000484550.21043.23.
Pełny tekst źródłaJung, G. S., i R. H. Park. "Automatic edge extraction using locally adaptive threshold". Electronics Letters 24, nr 11 (26.05.1988): 711–12. http://dx.doi.org/10.1049/el:19880480.
Pełny tekst źródłaRozprawy doktorskie na temat "Automatic threshold"
Braseth, Jørgen. "Automatic Configuration for Collective Construction : Automatic parameter setting for response threshold agents in collective construction". Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2007. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-8748.
Pełny tekst źródłaXie, Kaicheng. "Automatic Utility Meter Reading". Cleveland State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=csu1270587412.
Pełny tekst źródłaJeuthe, Julius. "Automatic Tissue Segmentation of Volumetric CT Data of the Pelvic Region". Thesis, Linköpings universitet, Medicinsk informatik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-133153.
Pełny tekst źródłaSILVA, Joberth de Nazaré. "Detecção automática de massas em mamografias digitais usando Quality Threshold clustering e MVS". Universidade Federal do Maranhão, 2013. http://tedebc.ufma.br:8080/jspui/handle/tede/1834.
Pełny tekst źródłaMade available in DSpace on 2017-08-16T18:29:06Z (GMT). No. of bitstreams: 1 JoberthSilva.pdf: 6383640 bytes, checksum: f18918eb45c49cb426b560e4daddf994 (MD5) Previous issue date: 2013-02-20
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Breast cancer is worldwide the most common form of cancer affecting woman, sometimes in their lives, at the proportion of either one to nine or one to thirteen women who reach the age of ninety in the west world (LAURENCE, 2006). Breast cancer is caused by frequent reproduction of cells in various parts of the human body. At certain times, and for reasons yet unknown, some cells begin to reproduce at a higher speed, causing the onset of cellular masses called neoplasias, or tumors, which are new tissue formation, but from pathological origin. This work has proposed a method of automatic detection of masses in digital mammograms, using the Quality Threshold (QT), and the Supporting Vector Machine (MVS). The images processing steps were as follows: firstly, the pre-processing phase took place which consisted of removing the background image, smoothing it with a low pass filter, to increase the degree of contrast, and then, in sequence, accomplishing an enhancement of the Wavelet Transform (WT) by changing their coefficients with a linear function. After the pre-processing phase, came the segmentation with the use of the QT which divided the image in to clusters with pre-defined diameters. Then, the post-processing occurred with the selection of the best candidates to mass formed by the MVS analysis of the shape descriptors. For the extraction phase of texture features the Haralick descriptors and the function correlogram were used. As for the classification stage, the MVS was used again for training, validation of the MVS model and final test. The achieved results were: sensitivity of 92. 31%, specificity of 82.2%, accuracy of 83,53%, a false positive rate per image of 1.12 and an area under a FROC curve of 0.8033.
O câncer de mama é, mundialmente, a forma mais comum de câncer em mulheres afetando, em algum momento suas vidas, aproximadamente uma em cada nove a uma em cada treze mulheres que atingem os noventa anos no mundo ocidental (LAURANCE, 2006). O câncer de mama é ocasionado pela reprodução frequente de células de diversas partes do corpo humano. Em certos momentos e por motivos ainda desconhecidos algumas células começam a se reproduzir com uma velocidade maior, ocasionando o surgimento de massas celulares denominadas de neoplasias ou tumores que são tecidos de formação nova, mas de origem patológica. Neste trabalho foi proposto um método de detecção automática de massas em mamografias digitais usando o Quality Threshold (QT), e a Máquina de Vetores de Suporte (MVS). As etapas de processamento das imagens foram as seguintes: primeiramente veio a fase de pré-processamento que consiste em retirar o fundo da imagem, suavizá-la com um filtro passa-baixa, aumentar a escala de contraste, e na sequencia realizar um realce com a Transformada de Wavelet (WT) através da alteração dos seus coeficientes com uma função linear. Após a fase de pré-processamento vem a seguimentação utilizando o QT que segmenta a imagem em clusters com diâmetros pré-definidos. Em seguida, vem o pós-processamento com a seleção dos melhores candidatos à massa feita através da análise dos descritores de forma pela MVS. Para fase de extração de características de textura foram utiliza os descritores de Haralick e a função correlograma. Já na fase de classificação a MVS novamente foi utilizada para o treinamento, validação do modelo MVS e teste final. Os resultados alcançados foram: sensibilidade de 92,31%, especificidade de 82,2%, Acurácia de 83,53%, uma taxa de falsos positivos por imagem de 1,12 e uma área sob a curva FROC de 0,8033.
Zhang, Zai Yong. "Simultaneous fault diagnosis of automotive engine ignition systems using pairwise coupled relevance vector machine, extracted pattern features and decision threshold optimization". Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2493967.
Pełny tekst źródłaAnderson, Foery Kristen R. "Triggering the Lombard effect: Examining automatic thresholds". Connect to online resource, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1460856.
Pełny tekst źródłaSchairer, Kim, Elizabeth Kolberg, Douglas H. Keefe, Denis Fitzpatrick, Daniel Putterman i Patrick Feeney. "Automated Wideband Acoustic Reflex Threshold Test". Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etsu-works/1803.
Pełny tekst źródłaDjiallis, Caroline Helen. "Variability of the automated perimetric threshold response". Thesis, Cardiff University, 2005. http://orca.cf.ac.uk/54548/.
Pełny tekst źródłaVan, Tonder Jessica Jacqueline. "Automated smartphone threshold audiometry : validity and time-efficiency". Diss., University of Pretoria, 2016. http://hdl.handle.net/2263/60435.
Pełny tekst źródłaDissertation (M Communication Pathology)--University of Pretoria, 2016.
Speech-Language Pathology and Audiology
M Communication Pathology
Unrestricted
Pierce, Luke. "NANOPIPELINED THRESHOLD SYNTHESIS USING GATE REPLICATION". OpenSIUC, 2011. https://opensiuc.lib.siu.edu/theses/694.
Pełny tekst źródłaKsiążki na temat "Automatic threshold"
Fortinsky, Michael. Eye-movements and automated threshold perimetry. Ottawa: National Library of Canada, 1990.
Znajdź pełny tekst źródłaBragg, Eric W. At the Threshold of Liquid Geology: And Other Automatic Tales. Writers Advantage, 2002.
Znajdź pełny tekst źródłaRimašauskas, Marius, Rūta Rimašauskienė i Tomas Kuncius. Additive Manufacturing. Guidelines for Laboratory Works. KTU leidykla „Technologija“, 2022. http://dx.doi.org/10.5755/e01.9786090217979.
Pełny tekst źródłaMann, Elizabeth C. L. An investigation into test frequency effects on the corrosion fatigue crack growth threshold of 7075-T6 aluminium-alloy using a personal computer based automated system. 1985.
Znajdź pełny tekst źródłaFinancial management: DOD needs to lower the disbursement prevalidation threshold : report to congressional requesters. Washington, D.C: The Office, 1996.
Znajdź pełny tekst źródłaGajewski, Zbigniew. Prognozowanie wystąpień faz fenologicznych pierwiosnki omączonej Primula farinosa L. (Primulaceae) – krytycznie zagrożonego gatunku - w odniesieniu do fenologii innych składników lokalnej flory i panujących warunków termicznych. Publishing House of the University of Agriculture in Krakow, 2018. http://dx.doi.org/10.15576/978-83-66602-32-8.
Pełny tekst źródłaCzęści książek na temat "Automatic threshold"
Wilkinson, Michael H. F. "Gaussian-Weighted Moving-Window Robust Automatic Threshold Selection". W Computer Analysis of Images and Patterns, 369–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45179-2_46.
Pełny tekst źródłaXie, Pengyi, Jiangbin Zheng, Qianru Wei i Yuke Wang. "Automatic Threshold Selection Method for SAR Edge Detection". W Advances in Brain Inspired Cognitive Systems, 530–39. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39431-8_51.
Pełny tekst źródłaWhitehead, Anthony, Prosenjit Bose i Robert Laganiere. "Feature Based Cut Detection with Automatic Threshold Selection". W Lecture Notes in Computer Science, 410–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27814-6_49.
Pełny tekst źródłaPark, Seung-Jin, Kyung-Sik Seo i Jong-An Park. "Automatic Hepatic Tumor Segmentation Using Statistical Optimal Threshold". W Lecture Notes in Computer Science, 934–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11428831_116.
Pełny tekst źródłaHu, Jianping, i Jindan Chen. "Near-Threshold XOR and XNOR Circuits". W 2011 International Conference in Electrics, Communication and Automatic Control Proceedings, 1675–81. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8849-2_217.
Pełny tekst źródłaJoaquín, Pérez, Pazos Rodolfo, Velez Laura i Guillermo Rodríguez. "Automatic Generation of Control Parameters for the Threshold Accepting Algorithm". W MICAI 2002: Advances in Artificial Intelligence, 118–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46016-0_13.
Pełny tekst źródłaM.D., Arpitha, Megha P. Arakeri i G. Ram Mohan Reddy. "An Approach for Color Edge Detection with Automatic Threshold Detection". W Lecture Notes in Computer Science, 117–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29280-4_13.
Pełny tekst źródłaLorbeer, Boris, Ana Kosareva, Bersant Deva, Dženan Softić, Peter Ruppel i Axel Küpper. "A-BIRCH: Automatic Threshold Estimation for the BIRCH Clustering Algorithm". W Advances in Big Data, 169–78. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47898-2_18.
Pełny tekst źródłaSeo, Kyung-Sik. "Improved Fully Automatic Liver Segmentation Using Histogram Tail Threshold Algorithms". W Lecture Notes in Computer Science, 822–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11428862_115.
Pełny tekst źródłaZhang, Gui-Mei, Jun Chu i Jun Miao. "Recognizing a Planar Curve Based on NRLCTI and Area Threshold". W 2011 International Conference in Electrics, Communication and Automatic Control Proceedings, 567–75. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8849-2_72.
Pełny tekst źródłaStreszczenia konferencji na temat "Automatic threshold"
de Azevedo, D. F. G., S. Helegda, F. Glock i T. Russomano. "Automatic DarkAdaptation Threshold Detection Algorithm". W 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1616844.
Pełny tekst źródłaMahmood, Z., G. Thoonen i P. Scheunders. "Automatic threshold selection for morphological attribute profiles". W IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2012. http://dx.doi.org/10.1109/igarss.2012.6352502.
Pełny tekst źródłaChang, Ku-Yaw, Hao-Han Zhang, Shao-Jer Chen, Lih-Shyang Chen i Jia-Hong Chen. "Automatic Colon Segmentation Using Isolated-Connected Threshold". W 2011 First International Conference on Robot, Vision and Signal Processing (RVSP). IEEE, 2011. http://dx.doi.org/10.1109/rvsp.2011.65.
Pełny tekst źródłaSubramanian, R., i Rajiv Mehrotra. "Automatic Threshold Selection Based On Information Gain". W SPIE International Symposium on Optical Engineering and Industrial Sensing for Advance Manufacturing Technologies, redaktor Wayne Wiitanen. SPIE, 1988. http://dx.doi.org/10.1117/12.947682.
Pełny tekst źródłaZhang, Tianxu, Xinsai Wang i Yuehuan Wang. "Automatic threshold estimation for gradient image segmentation". W Multispectral Image Processing and Pattern Recognition, redaktorzy Tianxu Zhang, Bir Bhanu i Ning Shu. SPIE, 2001. http://dx.doi.org/10.1117/12.441435.
Pełny tekst źródłaBejinariu, Silviu-Ioan, Hariton Costin, Florin Rotaru, Ramona Luca i Cristina Diana Nita. "Automatic multi-threshold image segmentation using metaheuristic algorithms". W 2015 International Symposium on Signals, Circuits and Systems (ISSCS). IEEE, 2015. http://dx.doi.org/10.1109/isscs.2015.7204016.
Pełny tekst źródłaKiwanuka, Fred N., i Michael H. F. Wilkinson. "Automatic Attribute Threshold Selection for Blood Vessel Enhancement". W 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.566.
Pełny tekst źródłaLu, Zhang. "Improved automatic white balance based on Otsu threshold". W 2012 National Conference on Computing and Communication Systems (NCCCS). IEEE, 2012. http://dx.doi.org/10.1109/ncccs.2012.6412996.
Pełny tekst źródłaAggoun, Amar, Mohammad K. Ibrahim i Mohammad F. Daemi. "New automatic threshold selection algorithm for edge detection". W Optical Tools for Manufacturing and Advanced Automation, redaktor David P. Casasent. SPIE, 1993. http://dx.doi.org/10.1117/12.150156.
Pełny tekst źródłaOlivo, Jean-Christophe. "Image segmentation by wavelet-based automatic threshold selection". W Visual Communications '93, redaktorzy Barry G. Haskell i Hsueh-Ming Hang. SPIE, 1993. http://dx.doi.org/10.1117/12.157872.
Pełny tekst źródłaRaporty organizacyjne na temat "Automatic threshold"
Baader, Franz, Oliver Fernández Gil i Pavlos Marantidis. Approximation in Description Logics: How Weighted Tree Automata Can Help to Define the Required Concept Comparison Measures in FL₀. Technische Universität Dresden, 2016. http://dx.doi.org/10.25368/2022.230.
Pełny tekst źródłaBecker, Sarah, Megan Maloney i Andrew Griffin. A multi-biome study of tree cover detection using the Forest Cover Index. Engineer Research and Development Center (U.S.), wrzesień 2021. http://dx.doi.org/10.21079/11681/42003.
Pełny tekst źródłaRoth, Christian. Evaluation of the In-vehicle Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, maj 2023. http://dx.doi.org/10.4271/epr2023009.
Pełny tekst źródłaCasper, Gary, Stefanie Nadeau i Thomas Parr. Acoustic amphibian monitoring, 2019 data summary: Isle Royale National Park. National Park Service, grudzień 2022. http://dx.doi.org/10.36967/2295506.
Pełny tekst źródłaCasper, Gary, Stefanie Nadeau i Thomas Parr. Acoustic amphibian monitoring, 2019 data summary: Sleeping Bear Dunes National Lakeshore. National Park Service, grudzień 2022. http://dx.doi.org/10.36967/2295512.
Pełny tekst źródłaCasper, Gary, Stefanie Nadeau i Thomas Parr. Acoustic amphibian monitoring, 2019 data summary: Pictured Rocks National Lakeshore. National Park Service, grudzień 2022. http://dx.doi.org/10.36967/2295509.
Pełny tekst źródłaCasper, Gary, Stfani Madau i Thomas Parr. Acoustic amphibian monitoring, 2019 data summary: Mississippi National River and Recreation Area. National Park Service, grudzień 2022. http://dx.doi.org/10.36967/2295507.
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