Academic literature on the topic 'Automatic threshold'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Automatic threshold.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Automatic threshold"
Waller, James K. "Intelligent automatic threshold circuit." Journal of the Acoustical Society of America 96, no. 1 (July 1994): 616. http://dx.doi.org/10.1121/1.410401.
Full textZhao, Shuang Ping, Xiang Wei Li, Jing Hong Xing, and Gong Zheng. "An Wavelet Image Automatic Threshold Selection Denoising Method." Advanced Materials Research 482-484 (February 2012): 780–83. http://dx.doi.org/10.4028/www.scientific.net/amr.482-484.780.
Full textMidena, E., P. P. Radin, E. Convento, and F. Cavarzeran. "Macular Automatic Fundus Perimetry Threshold versus Standard Perimetry Threshold." European Journal of Ophthalmology 17, no. 1 (January 2007): 63–68. http://dx.doi.org/10.1177/112067210701700109.
Full textAdali, Tulay. "Automatic threshold selection using histogram quantization." Journal of Biomedical Optics 2, no. 2 (April 1, 1997): 211. http://dx.doi.org/10.1117/12.268965.
Full textLopes, N. V., P. A. Mogadouro do Couto, H. Bustince, and P. Melo-Pinto. "Automatic Histogram Threshold Using Fuzzy Measures." IEEE Transactions on Image Processing 19, no. 1 (January 2010): 199–204. http://dx.doi.org/10.1109/tip.2009.2032349.
Full textWan, Yan, Li Yao, and Bugao Xu. "Automatic Segmentation of Fiber Cross Sections by Dual Thresholding." Journal of Engineered Fibers and Fabrics 7, no. 1 (March 2012): 155892501200700. http://dx.doi.org/10.1177/155892501200700113.
Full textKANATANI, KENICHI, and YASUSHI KANAZAWA. "AUTOMATIC THRESHOLDING FOR CORRESPONDENCE DETECTION." International Journal of Image and Graphics 04, no. 01 (January 2004): 21–33. http://dx.doi.org/10.1142/s0219467804001270.
Full textQiu, Y., A. R. Whittaker, M. Lucas, and K. Anderson. "Automatic wheeze detection based on auditory modelling." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 219, no. 3 (March 1, 2005): 219–27. http://dx.doi.org/10.1243/095441105x28551.
Full textBardy, Fabrice, Bram Van Dun, Harvey Dillon, Mark Seeto, Humphry Qin, Teck Loi, and Robert Cowan. "The Cortical Automatic Threshold Estimation in Adults." Hearing Journal 69, no. 6 (June 2016): 32. http://dx.doi.org/10.1097/01.hj.0000484550.21043.23.
Full textJung, G. S., and R. H. Park. "Automatic edge extraction using locally adaptive threshold." Electronics Letters 24, no. 11 (May 26, 1988): 711–12. http://dx.doi.org/10.1049/el:19880480.
Full textDissertations / Theses on the topic "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.
Full textXie, Kaicheng. "Automatic Utility Meter Reading." Cleveland State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=csu1270587412.
Full textJeuthe, 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.
Full textSILVA, 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.
Full textMade 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.
Full textAnderson, 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.
Full textSchairer, Kim, Elizabeth Kolberg, Douglas H. Keefe, Denis Fitzpatrick, Daniel Putterman, and Patrick Feeney. "Automated Wideband Acoustic Reflex Threshold Test." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etsu-works/1803.
Full textDjiallis, Caroline Helen. "Variability of the automated perimetric threshold response." Thesis, Cardiff University, 2005. http://orca.cf.ac.uk/54548/.
Full textVan, Tonder Jessica Jacqueline. "Automated smartphone threshold audiometry : validity and time-efficiency." Diss., University of Pretoria, 2016. http://hdl.handle.net/2263/60435.
Full textDissertation (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.
Full textBooks on the topic "Automatic threshold"
Fortinsky, Michael. Eye-movements and automated threshold perimetry. Ottawa: National Library of Canada, 1990.
Find full textBragg, Eric W. At the Threshold of Liquid Geology: And Other Automatic Tales. Writers Advantage, 2002.
Find full textRimašauskas, Marius, Rūta Rimašauskienė, and Tomas Kuncius. Additive Manufacturing. Guidelines for Laboratory Works. KTU leidykla „Technologija“, 2022. http://dx.doi.org/10.5755/e01.9786090217979.
Full textMann, 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.
Find full textFinancial management: DOD needs to lower the disbursement prevalidation threshold : report to congressional requesters. Washington, D.C: The Office, 1996.
Find full textGajewski, 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.
Full textBook chapters on the topic "Automatic threshold"
Wilkinson, Michael H. F. "Gaussian-Weighted Moving-Window Robust Automatic Threshold Selection." In 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.
Full textXie, Pengyi, Jiangbin Zheng, Qianru Wei, and Yuke Wang. "Automatic Threshold Selection Method for SAR Edge Detection." In 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.
Full textWhitehead, Anthony, Prosenjit Bose, and Robert Laganiere. "Feature Based Cut Detection with Automatic Threshold Selection." In 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.
Full textPark, Seung-Jin, Kyung-Sik Seo, and Jong-An Park. "Automatic Hepatic Tumor Segmentation Using Statistical Optimal Threshold." In Lecture Notes in Computer Science, 934–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11428831_116.
Full textHu, Jianping, and Jindan Chen. "Near-Threshold XOR and XNOR Circuits." In 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.
Full textJoaquín, Pérez, Pazos Rodolfo, Velez Laura, and Guillermo Rodríguez. "Automatic Generation of Control Parameters for the Threshold Accepting Algorithm." In 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.
Full textM.D., Arpitha, Megha P. Arakeri, and G. Ram Mohan Reddy. "An Approach for Color Edge Detection with Automatic Threshold Detection." In 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.
Full textLorbeer, Boris, Ana Kosareva, Bersant Deva, Dženan Softić, Peter Ruppel, and Axel Küpper. "A-BIRCH: Automatic Threshold Estimation for the BIRCH Clustering Algorithm." In Advances in Big Data, 169–78. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47898-2_18.
Full textSeo, Kyung-Sik. "Improved Fully Automatic Liver Segmentation Using Histogram Tail Threshold Algorithms." In Lecture Notes in Computer Science, 822–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11428862_115.
Full textZhang, Gui-Mei, Jun Chu, and Jun Miao. "Recognizing a Planar Curve Based on NRLCTI and Area Threshold." In 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.
Full textConference papers on the topic "Automatic threshold"
de Azevedo, D. F. G., S. Helegda, F. Glock, and T. Russomano. "Automatic DarkAdaptation Threshold Detection Algorithm." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1616844.
Full textMahmood, Z., G. Thoonen, and P. Scheunders. "Automatic threshold selection for morphological attribute profiles." In IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2012. http://dx.doi.org/10.1109/igarss.2012.6352502.
Full textChang, Ku-Yaw, Hao-Han Zhang, Shao-Jer Chen, Lih-Shyang Chen, and Jia-Hong Chen. "Automatic Colon Segmentation Using Isolated-Connected Threshold." In 2011 First International Conference on Robot, Vision and Signal Processing (RVSP). IEEE, 2011. http://dx.doi.org/10.1109/rvsp.2011.65.
Full textSubramanian, R., and Rajiv Mehrotra. "Automatic Threshold Selection Based On Information Gain." In SPIE International Symposium on Optical Engineering and Industrial Sensing for Advance Manufacturing Technologies, edited by Wayne Wiitanen. SPIE, 1988. http://dx.doi.org/10.1117/12.947682.
Full textZhang, Tianxu, Xinsai Wang, and Yuehuan Wang. "Automatic threshold estimation for gradient image segmentation." In Multispectral Image Processing and Pattern Recognition, edited by Tianxu Zhang, Bir Bhanu, and Ning Shu. SPIE, 2001. http://dx.doi.org/10.1117/12.441435.
Full textBejinariu, Silviu-Ioan, Hariton Costin, Florin Rotaru, Ramona Luca, and Cristina Diana Nita. "Automatic multi-threshold image segmentation using metaheuristic algorithms." In 2015 International Symposium on Signals, Circuits and Systems (ISSCS). IEEE, 2015. http://dx.doi.org/10.1109/isscs.2015.7204016.
Full textKiwanuka, Fred N., and Michael H. F. Wilkinson. "Automatic Attribute Threshold Selection for Blood Vessel Enhancement." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.566.
Full textLu, Zhang. "Improved automatic white balance based on Otsu threshold." In 2012 National Conference on Computing and Communication Systems (NCCCS). IEEE, 2012. http://dx.doi.org/10.1109/ncccs.2012.6412996.
Full textAggoun, Amar, Mohammad K. Ibrahim, and Mohammad F. Daemi. "New automatic threshold selection algorithm for edge detection." In Optical Tools for Manufacturing and Advanced Automation, edited by David P. Casasent. SPIE, 1993. http://dx.doi.org/10.1117/12.150156.
Full textOlivo, Jean-Christophe. "Image segmentation by wavelet-based automatic threshold selection." In Visual Communications '93, edited by Barry G. Haskell and Hsueh-Ming Hang. SPIE, 1993. http://dx.doi.org/10.1117/12.157872.
Full textReports on the topic "Automatic threshold"
Baader, Franz, Oliver Fernández Gil, and 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.
Full textBecker, Sarah, Megan Maloney, and Andrew Griffin. A multi-biome study of tree cover detection using the Forest Cover Index. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42003.
Full textRoth, Christian. Evaluation of the In-vehicle Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, May 2023. http://dx.doi.org/10.4271/epr2023009.
Full textCasper, Gary, Stefanie Nadeau, and Thomas Parr. Acoustic amphibian monitoring, 2019 data summary: Isle Royale National Park. National Park Service, December 2022. http://dx.doi.org/10.36967/2295506.
Full textCasper, Gary, Stefanie Nadeau, and Thomas Parr. Acoustic amphibian monitoring, 2019 data summary: Sleeping Bear Dunes National Lakeshore. National Park Service, December 2022. http://dx.doi.org/10.36967/2295512.
Full textCasper, Gary, Stefanie Nadeau, and Thomas Parr. Acoustic amphibian monitoring, 2019 data summary: Pictured Rocks National Lakeshore. National Park Service, December 2022. http://dx.doi.org/10.36967/2295509.
Full textCasper, Gary, Stfani Madau, and Thomas Parr. Acoustic amphibian monitoring, 2019 data summary: Mississippi National River and Recreation Area. National Park Service, December 2022. http://dx.doi.org/10.36967/2295507.
Full text