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Journal articles on the topic 'Automatic threshold'

Author: Grafiati

Published: 11 September 2023

Last updated: 26 July 2025

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1

Waller, James K. "Intelligent automatic threshold circuit." Journal of the Acoustical Society of America 96, no. 1 (1994): 616. http://dx.doi.org/10.1121/1.410401.

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2

Zhao, 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.

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A wavelet image automatic threshold selection denoising method based on wavelet transform and genetic algorithm. Firstly, wavelet transition is introduced to an original signal and selecting a wavelet and a level of wavelet decomposition. Secondly, the automatic thresholds of every level of wavelet resolved are obtained by using genetic algorithms. At the same time, the high coefficients of every level were quantized. Thirdly, inverse transition of the coefficients was processed and achieves the final resulting signals. Compared to traditional threshold methods, the proposed method has advanta

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3

Midena, 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 (2007): 63–68. http://dx.doi.org/10.1177/112067210701700109.

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4

Wan, Yan, Li Yao, and Bugao Xu. "Automatic Segmentation of Fiber Cross Sections by Dual Thresholding." Journal of Engineered Fibers and Fabrics 7, no. 1 (2012): 155892501200700. http://dx.doi.org/10.1177/155892501200700113.

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In a microscopic image, fiber cross sections are often surrounded by borders distinctively darker than their bodies and the background. Fiber borders can be utilized to separate cross-sections properly so that accurate fiber shape and size information can be obtained. Hence, locating correct fiber borders is one of the most critical steps in cross-sectional analysis for fiber characterization and identification. This paper introduces a dual-thresholding algorithm that performs automatic fiber border segmentation from noisy cross-sectional images. The dual thresholds include a low threshold cal

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5

Adali, Tulay. "Automatic threshold selection using histogram quantization." Journal of Biomedical Optics 2, no. 2 (1997): 211. http://dx.doi.org/10.1117/12.268965.

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6

Lopes, 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 (2010): 199–204. http://dx.doi.org/10.1109/tip.2009.2032349.

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7

Jablonski, Adam, and Krzysztof Mendrok. "Automatic threshold setting for anomaly detection." Mechanical Systems and Signal Processing 228 (April 2025): 112462. https://doi.org/10.1016/j.ymssp.2025.112462.

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8

KANATANI, KENICHI, and YASUSHI KANAZAWA. "AUTOMATIC THRESHOLDING FOR CORRESPONDENCE DETECTION." International Journal of Image and Graphics 04, no. 01 (2004): 21–33. http://dx.doi.org/10.1142/s0219467804001270.

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We study the problem of thresholding the residual of template matching as a preprocess for selecting the correct matches between feature points in two images. In order to determine the threshold dynamically, we introduce a statistical model of the residual and compute an optimal threshold according to that model. The model parameters are estimated from the histogram of the residuals of candidate matches. Using real images, we show that our method can substantially upgrade the quality of the initial matches by simply adjusting the threshold.

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9

Taro Takei and Hiroki Horita. "Analysis of Business Processes with Automatic Detection of KPI Thresholds and Process Discovery Based on Trace Variants." Research Briefs on Information and Communication Technology Evolution 9 (September 5, 2023): 59–76. http://dx.doi.org/10.56801/rebicte.v9i.157.

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One effective way to analyze an event log of a complex business process is to filter the event log by a KPI threshold and then use an analysis algorithm. Event logs filtered by KPI thresholds are simpler and easier to understand than the original business process. KPI thresholds have conventionally had to be set through a trial-and-error process, but existing research has proposed an automatic KPI threshold detection method that reduces the time and effort required to search for a threshold value. In the existing method, an event log is first divided by an arbitrary number k, and a business pr

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10

Ejofodomi, O'tega, and Godswill Ofualagba. "Design of a Real Time Feedback Automation Irrigation System." European Journal of Engineering Research and Science 4, no. 12 (2019): 117–20. http://dx.doi.org/10.24018/ejers.2019.4.12.1685.

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This paper presents the design of a real time feedback control automated irrigation systems that consists of monitoring units, control units, irrigation pipeline valves, and a network of irrigation pipeline. The monitoring units continuously measure the soil moisture content in the irrigation blocks, and if the moisture content drops below a predetermined threshold for the particular crop under production, it sends a wireless message to the control units controlling the pipeline valves along the water flow channel, causing the control units to open the valves leading to the water source and co

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Ejofodomi, O'tega, and Godswill Ofualagba. "Design of a Real Time Feedback Automation Irrigation System." European Journal of Engineering and Technology Research 4, no. 12 (2019): 117–20. http://dx.doi.org/10.24018/ejeng.2019.4.12.1685.

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This paper presents the design of a real time feedback control automated irrigation systems that consists of monitoring units, control units, irrigation pipeline valves, and a network of irrigation pipeline. The monitoring units continuously measure the soil moisture content in the irrigation blocks, and if the moisture content drops below a predetermined threshold for the particular crop under production, it sends a wireless message to the control units controlling the pipeline valves along the water flow channel, causing the control units to open the valves leading to the water source and co

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12

Bardy, Fabrice, Bram Van Dun, Harvey Dillon, et al. "The Cortical Automatic Threshold Estimation in Adults." Hearing Journal 69, no. 6 (2016): 32. http://dx.doi.org/10.1097/01.hj.0000484550.21043.23.

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13

Jung, G. S., and R. H. Park. "Automatic edge extraction using locally adaptive threshold." Electronics Letters 24, no. 11 (1988): 711–12. http://dx.doi.org/10.1049/el:19880480.

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14

Ahmad, Norazlina, and Zaidatun Tasir. "Threshold Value in Automatic Learning Style Detection." Procedia - Social and Behavioral Sciences 97 (November 2013): 346–52. http://dx.doi.org/10.1016/j.sbspro.2013.10.244.

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15

Henstock, P. V., and D. M. Chelberg. "Automatic gradient threshold determination for edge detection." IEEE Transactions on Image Processing 5, no. 5 (1996): 784–87. http://dx.doi.org/10.1109/83.499917.

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16

dos Santos, Juliana B., Carlos A. Heuser, Viviane P. Moreira, and Leandro K. Wives. "Automatic threshold estimation for data matching applications." Information Sciences 181, no. 13 (2011): 2685–99. http://dx.doi.org/10.1016/j.ins.2010.05.029.

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17

Razavi, H. M. "Self-purging redundancy with automatic threshold adjustment." IEE Proceedings G Circuits, Devices and Systems 140, no. 4 (1993): 233. http://dx.doi.org/10.1049/ip-g-2.1993.0040.

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18

Ueki, Masao, and Yoshinori Kawasaki. "Automatic grouping using smooth-threshold estimating equations." Electronic Journal of Statistics 5 (2011): 309–28. http://dx.doi.org/10.1214/11-ejs608.

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19

Olivo, J. C. "Automatic Threshold Selection Using the Wavelet Transform." CVGIP: Graphical Models and Image Processing 56, no. 3 (1994): 205–18. http://dx.doi.org/10.1006/cgip.1994.1019.

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20

Wen, Hui Jian, Jiao Lv, and Yao Kai Shi. "Automatic Identification of Vertical Fracture." Advanced Materials Research 962-965 (June 2014): 2961–64. http://dx.doi.org/10.4028/www.scientific.net/amr.962-965.2961.

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According to the changes of grayscale or colour in imaging logging data, the principle of fracture automatically segmentation which is in control of threshold and boundary is adopted, combing threshold segmentation principle with shapes of vertical fracture , we can segment and extract fracture images accurately. This method is applied to identification of vertical fracture in logging image which takes thin interacted layer horizontal bedding as background.

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21

Qiu, 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 (2005): 219–27. http://dx.doi.org/10.1243/095441105x28551.

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Automatic wheeze detection has several potential benefits compared with reliance on human auscultation: it is experience independent, an automated historical record can easily be kept, and it allows quantification of wheeze severity. Previous attempts to detect wheezes automatically have had partial success but have not been reliable enough to become widely accepted as a useful tool. In this paper an improved algorithm for automatic wheeze detection based on auditory modelling is developed, called the frequency- and duration-dependent threshold algorithm. The mean frequency and duration of eac

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22

Dr., Zaw Win Aung. "Automatic Attendance System Using Speaker Recognition." International Journal of Trend in Scientific Research and Development 2, no. 6 (2018): 802–6. https://doi.org/10.31142/ijtsrd18763.

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The main aim of this paper is to develop automatic attendance system using speaker recognition technique. The proposed system is software architecture which allows the user to access the system by making an utterance from microphone and the attendance of corresponding user is marked in the Microsoft Office Excel. The proposed system automates the whole process of taking attendance. The system uses text dependent open set speaker identification with MFCC features and vector quantization based speaker modeling for authenticating the user. A simple Euclidean distance scoring is used as the classi

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23

Cone-Wesson, Barbara, Richard C. Dowell, Dani Tomlin, Gary Rance, and Wu Jia Ming. "The Auditory Steady-State Response: Comparisons with the Auditory Brainstem Response." Journal of the American Academy of Audiology 13, no. 04 (2002): 173–87. http://dx.doi.org/10.1055/s-0040-1715962.

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Two studies are reported in which the threshold estimates from auditory steady-state response (ASSR) tests are compared to those of click- or toneburst-evoked auditory brainstem responses (ABRs). The first, a retrospective review of 51 cases, demonstrated that both the click-evoked ABR and the ASSR threshold estimates in infants and children could be used to predict the pure-tone threshold. The second, a prospective study of normal-hearing adults, provided evidence that the toneburst-evoked ABR and the modulated tone–evoked ASSR thresholds were similar when both were detected with an automatic

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24

Uyun, Shofwatul, Sri Hartati, Agus Harjoko, Subanar, and Lina Choridah. "Comparison between Automatic and Semiautomatic Thresholding Method for Mammographic Density Classification." Advanced Materials Research 896 (February 2014): 672–75. http://dx.doi.org/10.4028/www.scientific.net/amr.896.672.

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Mammographic density is a novel independent risk factor of breast cancer that reflects the amount of fibroglandular tissue. Breast Imaging Reporting and Data System (BIRADS) density is one of the mammographic density classification schemes which are most widely used by radiologists. Initially, the method used for assessing mammographic density was subjective and qualitative. Recently however, the measurement of mammographic density is more objective and quantitative. In this paper, we propose an alternative model of breast cancer risk factor assessment based on a quantitative approach of densi

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25

Heise, Stephan J., Jesko L. Verhey, and Manfred Mauermann. "Automatic screening and detection of threshold fine structure." International Journal of Audiology 47, no. 8 (2008): 520–32. http://dx.doi.org/10.1080/14992020802089473.

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26

Zou, Yaobin, Qingqing Huang, and Huikang Qi. "Automatic threshold selection guided by maximizing Pearson correlation." Computers and Electrical Engineering 120 (December 2024): 109815. http://dx.doi.org/10.1016/j.compeleceng.2024.109815.

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27

ANEGAWA, O. "Automatic Threshold Control for ASK Millimeter-Wave Transceivers." IEICE Transactions on Communications E88-B, no. 3 (2005): 1249–52. http://dx.doi.org/10.1093/ietcom/e88-b.3.1249.

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28

Gupta, Pallav, Rui Zhang, and Niraj K. Jha. "Automatic Test Generation for Combinational Threshold Logic Networks." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 16, no. 8 (2008): 1035–45. http://dx.doi.org/10.1109/tvlsi.2008.2000671.

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29

Singh, Dhananjay Kumar, and P. K. Panigrahi. "Automatic threshold technique for holographic particle field characterization." Applied Optics 51, no. 17 (2012): 3874. http://dx.doi.org/10.1364/ao.51.003874.

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30

SAUER, WILLIAM H., JOSHUA M. COOPER, REBECCA W. LAI, and RALPH J. VERDINO. "Underestimation of Pacing Threshold as Determined by an Automatic Ventricular Threshold Testing Algorithm." Pacing and Clinical Electrophysiology 29, no. 9 (2006): 1028–30. http://dx.doi.org/10.1111/j.1540-8159.2006.00483.x.

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31

Saha, Rupali, Roshani Thakre, Sakshi Khapekar, Shrushti Newalkar, Sarthak Nagdeote, and Viprant Moon. "Automatic Plant Watering System." International Journal for Research in Applied Science and Engineering Technology 11, no. 2 (2023): 431–36. http://dx.doi.org/10.22214/ijraset.2023.49041.

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Abstract: In India, most people depend on agricultural farming. A system is needed for resolving the many issues regarding the development of agricultural farming. Hence, an IoT based Automatic Plant Watering System. This system can be implemented on projects like green building concepts, roof farming, etc. The device will be connected to the internet so that the user can get updates about change in the moisture level. The two major parts of the water supply method is to know when and how much water to supply to the plants. This system is implemented such that it will sense the soil moisture c

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32

Ishida, Megumi. "Automatic thresholding for digital hemispherical photography." Canadian Journal of Forest Research 34, no. 11 (2004): 2208–16. http://dx.doi.org/10.1139/x04-103.

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This paper proposes an automatic thresholding method for the discrimination of sky and canopy elements in color hemispherical photographs taken with a digital camera (Nikon Coolpix 950). The exposures for photography were principally determined on the basis of zenith luminance. DIFphoto, which is diffuse transmittance calculated from the hemispherical photographs, was related to DIFsensor, which is diffuse transmittance measured directly with a photosynthetic photon flux density sensor. First, the thresholds for calculation of DIFphoto were manually assessed in the photographs to obtain the be

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33

Saleh, Marwan D., C. Eswaran, and Ahmed Mueen. "An Automated Blood Vessel Segmentation Algorithm Using Histogram Equalization and Automatic Threshold Selection." Journal of Digital Imaging 24, no. 4 (2010): 564–72. http://dx.doi.org/10.1007/s10278-010-9302-9.

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34

Kim, Jonghoek. "Automatic Thread Defect Examination System." Applied Sciences 12, no. 12 (2022): 6109. http://dx.doi.org/10.3390/app12126109.

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This article addresses a Thread Hole (TH) examination system capable of automatically examining a TH. We introduce a contact-based examination system with simple hardware. Our examination system examines TH defects by observing the torque generated when tightening the Inspection Device (ID) through the TH. A buffer with springs is utilized to overcome instances where the TH is not accurately aligned with the axis of the ID. The examination system observes the TH size utilizing a camera, then automatically performs an examination utilizing the optimal torque threshold associated with the TH siz

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35

Kang, Inshik, Hyunok Song, and Hoekyung Jung. "User command acquisition based iot automatic control system." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 1 (2019): 307–12. https://doi.org/10.11591/ijeecs.v13.i1.pp307-312.

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Recently, researches are being conducted to build a smart environment using various sensors and devices and to provide customized services to users through IoT (Internet of Things). The existing IoT system operates the device only by using the threshold value. So, there are problems that device operates when a service that does not consider a user characteristic is provided or when user not necessary. In this paper, to solve these problems, the system collects the remote control commands and compares it with the value of device operation threshold and sets the changed threshold value. In addit

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36

Hegazi, Aya, Ahmed Taha, and Mazen Mohamed Selim. "Copy-Move Forgery Detection Based on Automatic Threshold Estimation." International Journal of Sociotechnology and Knowledge Development 12, no. 1 (2020): 1–23. http://dx.doi.org/10.4018/ijskd.2020010101.

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Recently, users and news followers across websites face many fabricated images. Moreover, it goes far beyond that to the point of defaming or imprisoning a person. Hence, image authentication has become a significant issue. One of the most common tampering techniques is copy-move. Keypoint-based methods are considered as an effective method for detecting copy-move forgeries. In such methods, the feature extraction process is followed by applying a clustering technique to group spatially close keypoints. Most clustering techniques highly depend on the existence of a specific threshold to termin

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37

Ma, Xing, Jun Li Han, and Chang Shun Liu. "Research on CCD Infrared Image Threshold Segmentation." Applied Mechanics and Materials 220-223 (November 2012): 1292–97. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.1292.

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In recent years, the gray-scale thresholding segmentation has emerged as a primary tool for image segmentation. However, the application of segmentation algorithms to an image is often disappointing. Based on the characteristics analysis of infrared image, this paper develops several gray-scale thresholding segmentation methods capable of automatic segmentation in regions of pedestrians of infrared image. The approaches of gray-scale thresholding segmentation method are described. Then the experimental system is established by using the infrared CCD device for pedestrian image detection. The i

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38

Cheng, Ho-Chih, Min-Chie Chiu, Che-Min Chiu, and Cheng-Ying Yang. "Design and development of an automatic gas poisoning prevention and ventilation system." MATEC Web of Conferences 185 (2018): 00010. http://dx.doi.org/10.1051/matecconf/201818500010.

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As both carbon monoxide (CO) and carbon dioxide (CO2) exit during a fire, they are obvious indicators for the need to be alert to the possibility of fire. Another problem concerning poisoning by carbon monoxide (CO) is asphyxiation that occurs in houses during winter. The development of an automatic gas poisoning prevention system in conjunction with a ventilation function using carbon monoxide/carbon dioxide sensors would prove beneficial, necessary. As presented here, this system includes two gas sensors, an alarm, a ventilation device, a motor, and a rain-protection louver. The louver is ma

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39

Kang, Inshik, Hyunok Song, and Hoekyung Jung. "User command acquisition based IoT automatic control system." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 1 (2019): 307. http://dx.doi.org/10.11591/ijeecs.v13.i1.pp307-312.

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<p>Recently, researches are being conducted to build a smart environment using various sensors and devices and to provide customized services to users through IoT (Internet of Things). The existing IoT system operates the device only by using the threshold value. So, there are problems that device operates when a service that does not consider a user characteristic is provided or when user not necessary. In this paper, to solve these problems, the system collects the remote control commands and compares it with the value of device operation threshold and sets the changed threshold value.

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40

Ed-dhahraouy, Mohammed, Hicham Riri, Manal Ezzahmouly, Abdelmajid Elmoutaouakkil, Farid Bourzgui, and Hamid El Byad. "Threshold-Based Segmentation for Landmark Detection Using CBCT Images." International Journal of Online and Biomedical Engineering (iJOE) 19, no. 10 (2023): 169–76. http://dx.doi.org/10.3991/ijoe.v19i10.39489.

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The aim of this study is to examine the influence of threshold-based segmentation on the mean error of automatic landmark detection in 3D CBCT images. A GUI was developed for radiologists, allowing manual landmark identification and visualization of CBCT images. After a threshold-based segmentation, a semi-automatic algorithm for landmark detection was designed using the anatomic definition of each landmark. A step of 50 Hounsfield units was used for threshold variation to assess the detection error. 5 CBCT images were used to validate the proposed approach. The measurement of error detection

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41

Kim, Sang Kon, Seung Ho Lee, and Seung Woo Seo. "An automatic portscan detection system with adaptive threshold setting." Journal of Communications and Networks 12, no. 1 (2010): 74–85. http://dx.doi.org/10.1109/jcn.2010.6388436.

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42

BIFFI, MAURO, GIROLAMO SPITALI, MASSIMO STEFANO SILVETTI, et al. "Atrial Threshold Variability: Implications for Automatic Atrial Stimulation Algorithms." Pacing and Clinical Electrophysiology 30, no. 12 (2007): 1445–54. http://dx.doi.org/10.1111/j.1540-8159.2007.00890.x.

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43

HIIPPALA, ANITA, GERALD A. SERWER, EVA CLAUSSON, LYNN DAVENPORT, TRINA BRAND, and JUHA-MATTI HAPPONEN. "Automatic Atrial Threshold Measurement and Adjustment in Pediatric Patients." Pacing and Clinical Electrophysiology 33, no. 3 (2010): 309–13. http://dx.doi.org/10.1111/j.1540-8159.2009.02619.x.

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44

Liu, Yu. "Study on Automatic Threshold Selection Algorithm of Sensor Images." Physics Procedia 25 (2012): 1769–75. http://dx.doi.org/10.1016/j.phpro.2012.03.309.

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45

Kiwanuka, Fred N., and Michael H. F. Wilkinson. "Automatic attribute threshold selection for morphological connected attribute filters." Pattern Recognition 53 (May 2016): 59–72. http://dx.doi.org/10.1016/j.patcog.2015.11.012.

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46

Kishihara, Jun, Shinichi Niwano, Hidehira Fukaya, et al. "Pacing failure caused by automatic pacing threshold adjustment system." Journal of Arrhythmia 33, no. 6 (2017): 637–39. http://dx.doi.org/10.1016/j.joa.2017.05.005.

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47

CHEN, Ruo-han, Ke-ping CHEN, Fang-zheng WANG, Wei HUA, and Shu ZHANG. "Impact of automatic threshold capture on pulse generator longevity." Chinese Medical Journal 119, no. 11 (2006): 925–29. http://dx.doi.org/10.1097/00029330-200606010-00008.

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48

KENNERGREN, CHARLES, BERIT LARSSON, ÅSA UHRENIUS, and FREDRIK GADLER. "Clinical Experience with an Automatic Threshold Tracking Algorithm Study." Pacing and Clinical Electrophysiology 26, no. 12 (2003): 2219–24. http://dx.doi.org/10.1111/j.1540-8159.2003.00350.x.

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49

Cristina Bisi, Maria, Rita Stagni, and Gianni Gnudi. "Automatic detection of maximal oxygen uptake and ventilatory threshold." Computers in Biology and Medicine 41, no. 1 (2011): 18–23. http://dx.doi.org/10.1016/j.compbiomed.2010.11.001.

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50

Bach, Michael, Christina Schmitt, Miriam Kromeier, and Guntram Kommerell. "The Freiburg Stereoacuity Test: automatic measurement of stereo threshold." Graefe's Archive for Clinical and Experimental Ophthalmology 239, no. 8 (2001): 562–66. http://dx.doi.org/10.1007/s004170100317.

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