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Relevant bibliographies by topics / Thresholding

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Academic literature on the topic 'Thresholding'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 May 2024

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Journal articles on the topic "Thresholding"

1

Huang, Hui Xian, Juan Gong, and Te Zhang. "Method of Adaptive Wavelet Thresholding Used in Image Denoising." Advanced Materials Research 204-210 (February 2011): 1184–87. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.1184.

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According to multi-resolution analysis of wavelet threshold denoising principle, this paper presented two improved algorithms of continuity and adaptive threshold based on hard thresholding. The soft thresholding (hyperbolic thresholding) was used in the intervals after setting two thresholds, and the isolated points were removed according to the adjacent correlation coefficient during the processing. As a result, the hard thresholding’s shortcomings were reduced. The simulation results show that improved algorithms have both better visual effect and PSNR than the traditional approaches.

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Inoue, A., K. Yamamoto, and N. Mizoue. "Comparison of automatic and interactive thresholding of hemispherical photography." Journal of Forest Science 57, No. 2 (February 18, 2011): 78–87. http://dx.doi.org/10.17221/68/2010-jfs.

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This study presents the effects of operator bias and variation in interactive thresholding on the estimation of light environment using hemispherical photography. Twenty-one hemispherical photographs taken beneath a wide range of canopy densities were visually converted to binary images twice by 21 operators, and then the gap fraction was computed from the images. The interactive threshold varied greatly among the different operators and within a single operator, which resulted in a considerable operator bias and variation in the gap fraction. This study also compared three widely used automatic thresholding algorithms, which were installed in freely available software LIA for Win32 for analyzing hemispherical photography, with interactive thresholding using the same photographs. The median of the interactive threshold by repetitive interactive thresholdings from 21 operators was assumed to be correct for the comparison. The results indicated that MINIMUM was considered to be a better algorithm than the other ones installed in LIA32 when the gap fraction was over 10%. However, VARIANCE seemed to be superior to MINIMUM under the low gap fraction and the cloudy sky condition with dark and white clouds. This implied that MINIMUM or VARIANCE should be used for analyzing hemispherical photographs with LIA32. In conclusion, we need to pay attention to the selection of the automatic thresholding algorithm and the sky condition when taking hemispherical photographs.

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Rosin, Paul L. "Unimodal thresholding." Pattern Recognition 34, no. 11 (November 2001): 2083–96. http://dx.doi.org/10.1016/s0031-3203(00)00136-9.

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Pal, Nikhil R., and Sankar K. Pal. "Entropic thresholding." Signal Processing 16, no. 2 (February 1989): 97–108. http://dx.doi.org/10.1016/0165-1684(89)90090-x.

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Kim, Min Hee, and Michael G. Akritas. "Order thresholding." Annals of Statistics 38, no. 4 (August 2010): 2314–50. http://dx.doi.org/10.1214/09-aos782.

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Aviad, Z., and E. Lozinskii. "Semantic thresholding." Pattern Recognition Letters 5, no. 5 (May 1987): 321–28. http://dx.doi.org/10.1016/0167-8655(87)90073-0.

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Liu, Haoyang, and Rina Foygel Barber. "Between hard and soft thresholding: optimal iterative thresholding algorithms." Information and Inference: A Journal of the IMA 9, no. 4 (December 10, 2019): 899–933. http://dx.doi.org/10.1093/imaiai/iaz027.

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Abstract Iterative thresholding algorithms seek to optimize a differentiable objective function over a sparsity or rank constraint by alternating between gradient steps that reduce the objective and thresholding steps that enforce the constraint. This work examines the choice of the thresholding operator and asks whether it is possible to achieve stronger guarantees than what is possible with hard thresholding. We develop the notion of relative concavity of a thresholding operator, a quantity that characterizes the worst-case convergence performance of any thresholding operator on the target optimization problem. Surprisingly, we find that commonly used thresholding operators, such as hard thresholding and soft thresholding, are suboptimal in terms of worst-case convergence guarantees. Instead, a general class of thresholding operators, lying between hard thresholding and soft thresholding, is shown to be optimal with the strongest possible convergence guarantee among all thresholding operators. Examples of this general class includes $\ell _q$ thresholding with appropriate choices of $q$ and a newly defined reciprocal thresholding operator. We also investigate the implications of the improved optimization guarantee in the statistical setting of sparse linear regression and show that this new class of thresholding operators attain the optimal rate for computationally efficient estimators, matching the Lasso.

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Dinc, Imren, Semih Dinc, Madhav Sigdel, Madhu S. Sigdel, Marc L. Pusey, and Ramazan S. Aygun. "Super-Thresholding: Supervised Thresholding of Protein Crystal Images." IEEE/ACM Transactions on Computational Biology and Bioinformatics 14, no. 4 (July 1, 2017): 986–98. http://dx.doi.org/10.1109/tcbb.2016.2542811.

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Cui, Angang, Haizhen He, and Hong Yang. "A Designed Thresholding Operator for Low-Rank Matrix Completion." Mathematics 12, no. 7 (April 2, 2024): 1065. http://dx.doi.org/10.3390/math12071065.

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In this paper, a new thresholding operator, namely, designed thresholding operator, is designed to recover the low-rank matrices. With the change of parameter in designed thresholding operator, the designed thresholding operator can apply less bias to the larger singular values of a matrix compared with the classical soft thresholding operator. Based on the designed thresholding operator, an iterative thresholding algorithm for recovering the low-rank matrices is proposed. Numerical experiments on some image inpainting problems show that the proposed thresholding algorithm performs effectively in recovering the low-rank matrices.

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Desiani, Anita, Des Alwine Zayanti, Rifkie Primartha, Filda Efriliyanti, and Nur Avisa Calista Andriani. "Variasi Thresholding untuk Segmentasi Pembuluh Darah Citra Retina." Jurnal Edukasi dan Penelitian Informatika (JEPIN) 7, no. 2 (August 21, 2021): 255. http://dx.doi.org/10.26418/jp.v7i2.47205.

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Segmentasi pembuluh darah pada retina diperlukan pada deteksi dini penyakit Diabetic Retinopathy pada citra retina. Penelitian ini menggunakan tiga tahapan yaitu pre-processing, segmentasi dan post-processing yang akan membandingkan hasil dari 3 metode segmentasi yang menggunakan nilai Thresholding yaitu Adaptive Thresholding, Binary Thresholding, dan Otsu Thresholding. Hasil pengujian terhadap tiga metode yang digunakan menunjukan bahwa metode Binary Thresholding mendapat rata-rata akurasi, sensitivitas dan spesifisitas tertinggi yaitu 95%, 58%, 98%. Untuk Adaptive Thresholding mendapat rata-rata akurasi sebesar 91%, sensitivitas 36%, spesititiftas 97%. Dan metode Otsu Thresholding mendapatkan rata-rata akurasi 86%, sensitivitas 22%, dan spesifisitas 90%. Dari hasil ketiga metode ini dapat dilihat akurasi yang dihasilkan oleh metode Thresholding sudah sangat baik dalam melakukan segmentasi citra, tetapi nilai sensitivitas dari masing-masing metode Thresholding masih rendah. Hal ini dapat disimpulkan metode Thresholding masih sulit mendapatkan lebih banyak fitur pembuluh darah pada citra retina.

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Dissertations / Theses on the topic "Thresholding"

1

Pavlicova, Martina. "Thresholding FMRI images." The Ohio State University, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=osu1097769474.

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Pavlicová, Martina. "Thresholding FMRI images." Connect to this title online, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1097769474.

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Thesis (Ph. D.)--Ohio State University, 2004.
Title from first page of PDF file. Document formatted into pages; contains xvii, 109 p.; also includes graphics (some col.) Includes bibliographical references (p. 107-109). Available online via OhioLINK's ETD Center

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Prakash, Aravind. "Confidential Data Dispersion using Thresholding." Scholarly Repository, 2009. http://scholarlyrepository.miami.edu/oa_theses/232.

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With growing trend in "cloud computing" and increase in the data moving into the Internet, the need to store large amounts of data by service providers such as Google, Yahoo and Microsoft has increased over time. Now, more than ever, there is a need to efficiently and securely store large amounts of data. This thesis presents an implementation of a Ramp Scheme that confidentially splits a data file into a configurable number of parts or shares of equal size such that a subset of those shares can recover the data entirely. Furthermore, the implementation supports a threshold for data compromise and data verification to verify that the data parts have not been tampered with. This thesis addresses two key problems faced in large-scale data storage, namely, data availability and confidentiality.

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Granlund, Oskar, and Kai Böhrnsen. "Improving character recognition by thresholding natural images." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208899.

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The current state of the art optical character recognition (OCR) algorithms are capable of extracting text from images in predefined conditions. OCR is extremely reliable for interpreting machine-written text with minimal distortions, but images taken in a natural scene are still challenging. In recent years the topic of improving recognition rates in natural images has gained interest because more powerful handheld devices are used. The main problem faced dealing with recognition in natural images are distortions like illuminations, font textures, and complex backgrounds. Different preprocessing approaches to separate text from its background have been researched lately. In our study, we assess the improvement reached by two of these preprocessing methods called k-means and Otsu by comparing their results from an OCR algorithm. The study showed that the preprocessing made some improvement on special occasions, but overall gained worse accuracy compared to the unaltered images.
Dagens optisk teckeninläsnings (OCR) algoritmer är kapabla av att extrahera text från bilder inom fördefinierade förhållanden. De moderna metoderna har uppnått en hög träffsäkerhet för maskinskriven text med minimala förvrängningar, men bilder tagna i en naturlig scen är fortfarande svåra att hantera. De senaste åren har ett stort intresse för att förbättra tecken igenkännings algoritmerna uppstått, eftersom fler kraftfulla och handhållna enheter används. Det huvudsakliga problemet när det kommer till igenkänning i naturliga bilder är olika förvrängningar som infallande ljus, textens textur och komplicerade bakgrunder. Olika metoder för förbehandling och därmed separation av texten och dess bakgrund har studerats under den senaste tiden. I våran studie bedömer vi förbättringen som uppnås vid förbehandlingen med två metoder som kallas för k-means och Otsu genom att jämföra svaren från en OCR algoritm. Studien visar att Otsu och k-means kan förbättra träffsäkerheten i vissa förhållanden men generellt sett ger det ett sämre resultat än de oförändrade bilderna.

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Kovac, Arne. "Wavelet thresholding for unequally time-spaced data." Thesis, University of Bristol, 1999. http://hdl.handle.net/1983/2088715a-7792-4032-bb76-83e3b0389b94.

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Buck, Jonathan Gordon. "IMPROVED THRESHOLDING TECHNIQUE FOR THE MONOBIT RECEIVER." Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1183477928.

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Katakam, Nikhil. "Pavement crack detection system through localized thresholding /." Connect to full text in OhioLINK ETD Center, 2009. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=toledo1260820344.

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Thesis (M.S.)--University of Toledo, 2009.
Typescript. "Submitted as partial fulfillment of the requirements for The Master of Science in Engineering." "A thesis entitled"--at head of title. Bibliography: leaves 65-68.

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Hertz, Lois. "Robust image thresholding techniques for automated scene analysis." Diss., Georgia Institute of Technology, 1990. http://hdl.handle.net/1853/15050.

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Benson, Stephen R. "Adaptive Thresholding for Detection of Radar Receiver Signals." Wright State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=wright1287692780.

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Liu, Feiran. "High Frequency Resolution Adaptive Thresholding Wideband Receiver System." Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1451042587.

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Books on the topic "Thresholding"

1

Jeunmeure, L. F. C. Intelligent thresholding. Manchester: UMIST, 1995.

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Erickson, Stephen A. The (Coming) Age of Thresholding. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-015-9271-0.

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Jansen, Maarten. Noise Reduction by Wavelet Thresholding. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0145-5.

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Erickson, Stephen A. The (coming) age of thresholding. Dordrecht: Kluwer Academic Publishers, 1999.

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Bayat, Niloofar. Internet Subscription Plans: Thresholding, Throttling, and Zero-Rating. [New York, N.Y.?]: [publisher not identified], 2022.

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6

Thresholding. Dorrance Pub Co, 1997.

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Erickson, S. A. (Coming) Age of Thresholding. Springer London, Limited, 2013.

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Erickson, S. A. The Age of Thresholding. Springer, 2010.

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Erickson, S. A. The Age of Thresholding. Springer, 2012.

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Jansen, Maarten. Noise Reduction by Wavelet Thresholding. Springer London, Limited, 2012.

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Book chapters on the topic "Thresholding"

1

Weik, Martin H. "thresholding." In Computer Science and Communications Dictionary, 1782. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_19568.

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Al Najjar, Mayssaa, Milad Ghantous, and Magdy Bayoumi. "Hysteresis Thresholding." In Lecture Notes in Electrical Engineering, 147–74. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-1857-3_7.

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Burger, Wilhelm, and Mark J. Burge. "Automatic Thresholding." In Texts in Computer Science, 253–89. London: Springer London, 2016. http://dx.doi.org/10.1007/978-1-4471-6684-9_11.

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Mitchell, H. B. "Image Thresholding." In Image Fusion, 155–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11216-4_12.

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Burger, Wilhelm, and Mark J. Burge. "Automatic Thresholding." In Principles of Digital Image Processing, 5–50. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-84882-919-0_2.

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Denz, Cornelia. "Nonlinear Thresholding." In Optical Neural Networks, 216–43. Wiesbaden: Vieweg+Teubner Verlag, 1998. http://dx.doi.org/10.1007/978-3-663-12272-2_5.

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Caponetti, Laura, and Giovanna Castellano. "Image Thresholding." In SpringerBriefs in Electrical and Computer Engineering, 121–32. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44130-6_9.

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Burger, Wilhelm, and Mark J. Burge. "Automatic Thresholding." In Texts in Computer Science, 241–77. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05744-1_9.

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Russ, John C. "Discrimination and Thresholding." In Computer-Assisted Microscopy, 99–127. Boston, MA: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4613-0563-7_5.

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Ting, Michael. "Iterative Thresholding Methods." In Molecular Imaging in Nano MRI, 15–42. Hoboken, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118760949.ch3.

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Conference papers on the topic "Thresholding"

1

Al-Bayati, Moumena, and Ali El-Zaart. "Mammogram images thresholding using different thresholding techniques." In 2012 2nd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA). IEEE, 2012. http://dx.doi.org/10.1109/ictea.2012.6462871.

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Olhede, Sofia C. "Hyperanalytic Thresholding." In 2006 International Conference on Image Processing. IEEE, 2006. http://dx.doi.org/10.1109/icip.2006.312693.

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Salaudeen, Olawale. "Multiclass Thresholding." In Proposed for presentation at the Sandia MLDL Conference in , . US DOE, 2021. http://dx.doi.org/10.2172/1888732.

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Xu, Jie, and Craig S. Kaplan. "Artistic thresholding." In the 6th international symposium. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1377980.1377990.

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Gu, Claire, and Pochi Yeh. "Optical Thresholding and Max Operation." In Optical Computing. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/optcomp.1991.me3.

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Thresholding and Max operations are essential elements in the implementation of neural networks. Although there have been several optical implementations of neural networks, the thresholding functions are performed electronically [1-3]. Optical thresholding and Max operations have the advantages of parallelism and cascadability without resorting to optoelectronic conversion. Unfortunately, there has been very limited work in this area. In this paper, we propose and study the properties of self-oscillation in nonlinear optical (NLO) four-wave mixing (FWM) and NLO resonators for parallel optical thresholding and Max operations.

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Wolff, Ran. "Distributed Convex Thresholding." In PODC '15: ACM Symposium on Principles of Distributed Computing. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2767386.2767387.

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Zhang, Zhuo, Junhao Gan, Zhifeng Bao, Seyed Mohammad Hussein Kazemi, Guangyong Chen, and Fengyuan Zhu. "Approximate Range Thresholding." In SIGMOD/PODS '22: International Conference on Management of Data. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3514221.3526123.

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Malioutov, Dmitry, and Aleksandr Aravkin. "Iterative log thresholding." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6854997.

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Kowalski, Matthieu. "Thresholding RULES and iterative shrinkage/thresholding algorithm: A convergence study." In 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7025843.

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Fu, Rong, Jie Zhang, RunFeng Wang, and Tao Xu. "Improved Wavelet Thresholding Function and Adaptive Thresholding for Noise Reduction." In 2023 11th International Conference on Intelligent Computing and Wireless Optical Communications (ICWOC). IEEE, 2023. http://dx.doi.org/10.1109/icwoc57905.2023.10200530.

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Reports on the topic "Thresholding"

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Kyrillidis, Anastasios, and Volkan Cevher. Matrix Recipes for Hard Thresholding Methods. Fort Belvoir, VA: Defense Technical Information Center, November 2012. http://dx.doi.org/10.21236/ada585818.

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Schick, I. C., and H. Krim. Robust Wavelet Thresholding for Noise Suppression. Fort Belvoir, VA: Defense Technical Information Center, December 1996. http://dx.doi.org/10.21236/ada458897.

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Dilworth, S. J., N. J. Kalton, D. Kutzarova, and V. N. Temlyakov. The Thresholding Greedy Algorithm, Greedy Bases and Duality. Fort Belvoir, VA: Defense Technical Information Center, August 2001. http://dx.doi.org/10.21236/ada640677.

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Beauchemin, M., and K. B. Fung. Image Thresholding Based on Spatial Variation Attribute Similarity. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2003. http://dx.doi.org/10.4095/220053.

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Abdallah, Mahmoud A., and Ram-Nandan P. Singh. Image Data Compression by Adaptive Thresholding of Wavelet Coefficients. Fort Belvoir, VA: Defense Technical Information Center, January 1999. http://dx.doi.org/10.21236/ada375823.

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Demuth, M. N., and M. Ednie. A glacier condition and thresholding rubric for use in assessing protected area / ecosystem functioning. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2016. http://dx.doi.org/10.4095/297892.

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Burris, H. R., C. I. Moore, L. A. Swingen, L. M. Wasiczko, R. Mahon, M. F. Stell, M. R. Suite, et al. Laboratory Implementation of an Adaptive Thresholding System for Free-Space Optical Communication Receivers with Signal Dependent Noise. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada472203.

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Cheng, Peng, James V. Krogmeier, Mark R. Bell, Joshua Li, and Guangwei Yang. Detection and Classification of Concrete Patches by Integrating GPR and Surface Imaging. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317320.

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This research considers the detection, location, and classification of patches in concrete and asphalt-on-concrete pavements using data taken from ground penetrating radar (GPR) and the WayLink 3D Imaging System. In particular, the project seeks to develop a patching table for “inverted-T” patches. A number of deep neural net methods were investigated for patch detection from 3D elevation and image observation, but the success was inconclusive, partly because of a dearth of training data. Later, a method based on thresholding IRI values computed on a 12-foot window was used to localize pavement distress, particularly as seen by patch settling. This method was far more promising. In addition, algorithms were developed for segmentation of the GPR data and for classification of the ambient pavement and the locations and types of patches found in it. The results so far are promising but far from perfect, with a relatively high rate of false alarms. The two project parts were combined to produce a fused patching table. Several hundred miles of data was captured with the Waylink System to compare with a much more limited GPR dataset. The primary dataset was captured on I-74. A software application for MATLAB has been written to aid in automation of patch table creation.

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Cheng, Peng, James V. Krogmeier, Mark R. Bell, Joshua Li, and Guangwei Yang. Detection and Classification of Concrete Patches by Integrating GPR and Surface Imaging. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317320.

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This research considers the detection, location, and classification of patches in concrete and asphalt-on-concrete pavements using data taken from ground penetrating radar (GPR) and the WayLink 3D Imaging System. In particular, the project seeks to develop a patching table for “inverted-T” patches. A number of deep neural net methods were investigated for patch detection from 3D elevation and image observation, but the success was inconclusive, partly because of a dearth of training data. Later, a method based on thresholding IRI values computed on a 12-foot window was used to localize pavement distress, particularly as seen by patch settling. This method was far more promising. In addition, algorithms were developed for segmentation of the GPR data and for classification of the ambient pavement and the locations and types of patches found in it. The results so far are promising but far from perfect, with a relatively high rate of false alarms. The two project parts were combined to produce a fused patching table. Several hundred miles of data was captured with the Waylink System to compare with a much more limited GPR dataset. The primary dataset was captured on I-74. A software application for MATLAB has been written to aid in automation of patch table creation.

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Hammouti, A., S. Larmagnat, C. Rivard, and D. Pham Van Bang. Use of CT-scan images to build geomaterial 3D pore network representation in preparation for numerical simulations of fluid flow and heat transfer, Quebec. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331502.

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Non-intrusive techniques such as medical CT-Scan or micro-CT allow the definition of 3D connected pore networks in porous materials, such as sedimentary rocks or concrete. The definition of these networks is a key step towards the evaluation of fluid flow and heat transfer in energy resource (e.g., hydrocarbon and geothermal reservoirs) and CO2 sequestration research projects. As material heterogeneities play a role at all scales (from micro- to project-scale), numerical models represent a powerful tool for bridging the gap between small-scale measurements provided by X-ray imaging techniques and larger-scale transport properties. This study uses pre-existing medical CT-scan datasets of reference material, namely glass beads and conventional reservoir rocks (Berea sandstone, Boise sandstone, Indiana limestone) to extract the 3D geometry of connected pores using an open-source software (Spam). Pore networks from rock samples were generated from dry and then saturated samples. Binarized datasets were produced for these materials (generated by a thresholding technique) to obtain pore size distribution and tortuosity, as well as preferential paths for fluid flow. Average porosities were also calculated for comparison with those obtained by conventional commercial laboratory techniques. The results obtained show that this approach works well for medium and coarse-grained materials that do not contain a large percentage of fine particles. However, this approach does not allow representative networks to be obtained for fine-grained rocks, due to the fact that small pores (or pore throats) cannot be taken into account in the datasets obtained from the medical CT-Scan. A next step, using datasets produced from a micro- CT scan, is planned in order to be able to generate representative networks in this type of material as well.

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