On the Threshold Method for Rainfall Estimation: Choosing the Optimal Threshold Level

1991 ◽  
Vol 86 (415) ◽  
pp. 626-633 ◽  
Author(s):  
Benjamin Kedem ◽  
Harry Pavlopoulos
Keyword(s):  
Threshold Level ◽  
Optimal Threshold ◽  
Threshold Method ◽  
Rainfall Estimation

scholarly journals Detection of signals of moving objects based on the time selection method

2021 ◽  
Vol 66 (3) ◽  
pp. 335-342
Author(s):  
V. M. Artemiev ◽  
S. M. Kostromitsky ◽  
A. O. Naumov
Keyword(s):  
Moving Objects ◽  
Average Duration ◽  
Threshold Level ◽  
Selection Method ◽  
Probability Of Detection ◽  
False Alarms ◽  
Threshold Method ◽  
Detection Of Signals ◽  
Two Stages ◽  
Time Selection

To increase the efficiency of detecting moving objects in radiolocation, additional features are used, associated with the characteristics of trajectories. The authors assumed that trajectories are correlated, that allows extrapolation of the coordinate values taking into account their increments over the scanning period. The detection procedure consists of two stages. At the first, detection is carried out by the classical threshold method with a low threshold level, which provides a high probability of detection with high values of the probability of false alarms. At the same time uncertainty in the selection of object trajectory embedded in false trajectories arises. Due to the statistical independence of the coordinates of the false trajectories in comparison with the correlated coordinates of the object, the average duration of the first of them is less than the average duration of the second ones. This difference is used to solve the detection problem at the second stage based on the time-selection method. The obtained results allow estimation of the degree of gain in the probability of detection when using the proposed method.

Segmentation of LED Pixel Matrix by Using Optimal Threshold Method with Area Constraint

2012 ◽  
Vol 27 (5) ◽  
pp. 653-657
Author(s):  
于洪春 YU Hong-chun ◽  
邓意成 DENG Yi-cheng ◽  
郑喜凤 ZHENG Xi-feng
Keyword(s):  
Optimal Threshold ◽  
Threshold Method ◽  
Pixel Matrix

Image Segmentation Method for Wheel Set Online Measurement

2010 ◽  
Vol 44-47 ◽  
pp. 3854-3858
Author(s):  
Kai Hua Wu ◽  
Tao Ban
Keyword(s):  
Image Segmentation ◽  
Measurement Accuracy ◽  
Image Acquisition ◽  
Measuring Method ◽  
Optimal Threshold ◽  
Online Measurement ◽  
Threshold Method ◽  
Iterative Analysis ◽  
Entropy Criterion ◽  
Image Segmentation Algorithm

Wheel set is the major running components of a train. Online measurement of wheel set wear parameters is important for the safety of train. The acquisition and processing of wheel set profile image is the key problem in an online measuring method based on machine vision. Factors influencing the effect of image acquisition were analyzed. The main factors included environmental illumination, light reflection, CCD exposure time and light source change. An optimal threshold method based on entropy criterion and genetic algorithm for image threshold segmentation was proposed. The optimal threshold was found by iterative analysis. The image segmentation algorithm eliminated effectively the interferences in the image acquisition and extracted wheel set profile curve from varying background. Better segmentation effect ensured the measurement accuracy of wheel set wear parameters.

scholarly journals Methods of grain separation from single-layer grinding wheel topography

Mechanik ◽  
2018 ◽  
Vol 91 (10) ◽  
pp. 926-928 ◽  
Author(s):  
Anna Bazan ◽  
Andrzej Kawalec
Keyword(s):  
Single Layer ◽  
Threshold Level ◽  
Measured Data ◽  
Average Level ◽  
Grinding Wheel ◽  
Watershed Segmentation ◽  
Threshold Method ◽  
Wheel Topography ◽  
Grinding Wheel Topography ◽  
Watershed Method

The paper presents results of research concerning methods used for determining grain separation from measured data representing single-layer grinding wheel topography. The methods apply information concerning determined threshold level (Threshold method) or concerning watershed segmentation (Watershed method). A method for determining the average level of bond was also proposed.

Water Film Detection in Water Distribution Test of PCCS

2013 ◽  
Vol 385-386 ◽  
pp. 663-667
Author(s):  
Xiao Feng Li
Keyword(s):  
Water Distribution ◽  
Cooling System ◽  
Motion Segmentation ◽  
Water Film ◽  
Optimal Threshold ◽  
Segmentation Method ◽  
Threshold Method ◽  
Illumination Change ◽  
Complex Background

In the passive containment cooling system (PCCS), water distribution tests are essential to verify the water distribution devices performance. Without regular boundaries and homogeneous intensities, images of water film acquired from these tests are hard to be detected by conventional approaches. We propose an improved segmentation method to identify the water film areas from the complex background. Considering the gray distortion resulted from asymmetric illumination, the method combines the modified motion segmentation and optimal threshold method. Detection results show that this method is hardly affected by the illumination change, and also insensitive to noise.

scholarly journals ANALYSIS OF WAVEFORM RETRACKING METHODS IN ANTARCTIC ICE SHEET BASED ON CRYOSAT-2 DATA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1561-1567
Author(s):  
F. Xiao ◽  
F. Li ◽  
S. Zhang ◽  
W. Hao ◽  
L. Yuan ◽  
...  
Keyword(s):  
Satellite Altimetry ◽  
Ice Sheet ◽  
Threshold Level ◽  
Radar Altimeter ◽  
Threshold Method ◽  
Dome A ◽  
Antarctic Ice Sheet ◽  
Waveform Data ◽  
Zhongshan Station ◽  
Waveform Retracking

Satellite altimetry plays an important role in many geoscientific and environmental studies of Antarctic ice sheet. The ranging accuracy is degenerated near coasts or over nonocean surfaces, due to waveform contamination. A postprocess technique, known as waveform retracking, can be used to retrack the corrupt waveform and in turn improve the ranging accuracy. In 2010, the CryoSat-2 satellite was launched with the Synthetic aperture Interferometric Radar ALtimeter (SIRAL) onboard. Satellite altimetry waveform retracking methods are discussed in the paper. Six retracking methods including the OCOG method, the threshold method with 10 %, 25 % and 50 % threshold level, the linear and exponential 5-β parametric methods are used to retrack CryoSat-2 waveform over the transect from Zhongshan Station to Dome A. The results show that the threshold retracker performs best with the consideration of waveform retracking success rate and RMS of retracking distance corrections. The linear 5-β parametric retracker gives best waveform retracking precision, but cannot make full use of the waveform data.

scholarly journals An Infrared Image Target Segmentation Based on Improved Threshold Method

2021 ◽  
Vol 15 ◽  
pp. 820-828
Author(s):  
Manzeng Ma ◽  
Dan Liu ◽  
Ruirui Zhang
Keyword(s):  
Image Segmentation ◽  
Target Detection ◽  
Infrared Imaging ◽  
Infrared Image ◽  
Operation Time ◽  
Optimal Threshold ◽  
Threshold Method ◽  
Intelligent Algorithms ◽  
Infrared Image Segmentation ◽  
Detection And Recognition

In recent years, infrared images have been applied in more and more extensive fields and the current research of infrared image segmentation and recognition can’t satisfy the needs of practical engineering applications. The interference of various factors on infrared detectors result in the targets detected presenting the targets of low contrast, low signal-to-noise ratio (SNR) and fuzzy edges on the infrared image, thus increasing the difficulty of target detection and recognition; therefore, it is the key point to segment the target in an accurate and complete manner when it comes to infrared target detection and recognition and it has great importance and practical value to make in-depth research in this respect. Intelligent algorithms have paved a new way for infrared image segmentation. To achieve target detection, segmentation, recognition and tracking with infrared imaging infrared thermography technology mainly analyzes such features as the grayscale, location and contour information of both background and target of infrared image, segments the target from the background with the help of various tools, extracts the corresponding target features and then proceeds recognition and tracking. To seek the optimal threshold of an image can be seen as to find the optimum value of a confinement problem. As to seek the threshold requires much computation, to seek the threshold through intelligent algorithms is more accurate. This paper proposes an automatic segmentation method for infrared target image based on differential evolution (DE) algorithm and OTSU. This proposed method not only takes into consideration the grayscale information of the image, but also pays attention to the relevant information of neighborhood space to facilitate more accurate image segmentation. After determining the scope of the optimal threshold, it integrates DE’s ability of globally searching the optimal solution. This method can lower the operation time and improve the segmentation efficiency. The simulation experiment proves that this method is very effective.

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