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threshold selection Method Based on Reciprocal Gray Entropy and Artificial Bee Colony Optimization

Transactions of Nanjing University of Aeronautics and Astronautics 2014年第4期31卷 362-369页

作者：吴一全孟天亮吴诗婳卢文平 College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Engineering Technology Research Center of Wuhan Intelligent Basin Changjiang River Scientific Research InstituteChangjiang Water Resources Commission of the Ministry of Water Resources Key Laboratory of the Yellow River Sediment of Ministry of Water Resource Yellow River Institute of Hydraulic Research State Key Laboratory of Urban Water Resource and Environment Harbin Institute of Technology State Key Laboratory of Food Science and Technology Jiangnan University

Since the logarithmic form of Shannon entropy has the drawback of undefined value at zero points,and most existing threshold selection methods only depend on the probability information,ignoring the within-class uniformity of gray level,a method of reciprocal gray entropy threshold selection is proposed based on two-dimensional(2-D)histogram region oblique division and artificial bee colony(ABC)***,the definition of reciprocal gray entropy is *** on the basis of one-dimensional(1-D)method,2-D threshold selection criterion function based on reciprocal gray entropy with histogram oblique division is *** accelerate the progress of searching the optimal threshold,the recently proposed ABC optimization algorithm is *** proposed method not only avoids the undefined value points in Shannon entropy,but also achieves high accuracy and anti-noise performance due to reasonable 2-D histogram region division and the consideration of within-class uniformity of gray level.A large number of experimental results show that,compared with the maximum Shannon entropy method with 2-D histogram oblique division and the reciprocal entropy method with 2-D histogram oblique division based on niche chaotic mutation particle swarm optimization(NCPSO),the proposed method can achieve better segmentation results and can satisfy the requirement of real-time processing.

关键词： image processing threshold selection reciprocal gray entropy 2-D histogram oblique division artificial bee colony (ABC) optimization algorithm

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A method for threshold selection in load extrapolation based on multiple criteria decision-making technology

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International Journal of Modeling, Simulation, and Scientific Computing 2017年第3期8卷 21-38页

作者： Jixin Wang Qingsong Liu Xinting Zhai School of Mechanical Science and Engineering Jilin University No.5988 Renmin Street ChangchunJilin 130022Renmin Street Nanguan DistrictP.R.China

In order to establish an accurate peak over threshold(POT)model for reasonable load extrapolation,a new threshold selection method based on multiple criteria decision making(MCDM)technology is *** fitting test criterion is taken into consideration in the *** each candidate threshold,the fitting values of several fitting test criteria are integrated into a comprehensive evaluation value through entropy method and MCDM *** threshold corresponding to the minimum comprehensive evaluation value is assumed as the optimal threshold.A random simulation study is carried out to evaluate the performance of the method and to compare them with other literature *** load data are applied the proposed *** the results shown that the proposed method could be seen as an additional method that complements existing threshold selection methods.

关键词： Load extrapolation threshold selection fitting test criteria multiple criteria decision-making technology comprehensive evaluation value

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An Effective Method of threshold selection for Small Object Image

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Defence Technology（防务技术） 2011年第4期7卷 235-242页

作者：吴一全吴加明占必超 School of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Science and Technology on Electro-optic Control Laboratory Institute of Electro-optic Equipment of AVIC State Key Laboratory of Novel Software Technology Nanjing University

The image segmentation difficulties of small objects which are much smaller than their background often occur in target detection and recognition. The existing threshold segmentation methods almost fail under the circumstances. Thus, a threshold selection method is proposed on the basis of area difference between background and object and intra-class variance. The threshold selection formulae based on one-dimensional (1-D) histogram, two-dimensional (2-D) histogram vertical segmentation and 2-D histogram oblique segmentation are given. A fast recursive algorithm of threshold selection in 2-D histogram oblique segmentation is derived. The segmented images and processing time of the proposed method are given in experiments. It is compared with some fast algorithms, such as Otsu, maximum entropy and Fisher threshold selection methods. The experimental results show that the proposed method can effectively segment the small object images and has better anti-noise property.

关键词： information processing small infrared target detection image segmentation threshold selection 2-D histogram oblique segmentation fast recursive algorithm

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Spatial pattern recognition for near-surface high temperature increases in mountain areas using MODIS and SRTM DEM

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Journal of Mountain Science 2024年第6期21卷 2025-2042页

作者： WANG Yanxia YANG Lisha HUANG Xiaoyuan ZHOU Ruliang School of Soil and Water Conservation Southwest Forestry UniversityKunming 650224China School of Economics and Management Southwest Forestry UniversityKunming 650224China

Abrupt near-surface temperature changes in mountainous areas are a special component of the mountain climate *** and accurate measurements of the locations,intensity,and width of the near-surface changes are necessary but highly difficult due to the complicated environmental conditions and instrumental *** paper develops a spatial pattern recognition method to measure the near-surface high temperature increase(NSHTI),one of the lesser-attended ***,raster window measurement was proposed to calculate the temperature lapse rate using MODIS land surface temperature and SRTM DEM *** fully considers the terrain heights of two neighboring cells on opposite or adjacent slopes with a moving window of 3×3 cell ***,a threshold selection was performed to identify the NSHTI cells using a threshold of-0.65℃/100 ***,the NSHTI strips were parameterized through raster vectorization and spatial *** Yunnan,a mountainous province in southwestern China,as the study area,the results indicate that the NSHTI cells concentrate in a strip-like pattern along the mountains and valleys,and the strips are almost parallel to the altitude contours with a slight northward ***,they are located mostly at a 3/5 height of high mountains or within 400 m from the valley floors,where the controlling topographic index is the altitude of the terrain trend surface but not the absolute elevation and the topographic uplift height and cutting ***,the NSHTI intensity varies with the geographic locations and the proportions increase with an exponential trend,and the horizontal width has a mean of about 1000 m and a maximum of over 5000 *** result demonstrates that the proposed method can effectively recognize NSHTI boundaries over mountains,providing support for the modeling of weather and climate systems and the development of mountain resources.

关键词： High temperature increase Mountain areas MODIS Spatial pattern recognition Raster window measurement threshold selection

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Numerical assessment of the erosion risk for cavitating twisted hydrofoil by three methods

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Journal of Hydrodynamics 2021年第4期33卷 698-711页

作者： Ting-ting Lei Huai-yu Cheng Bin Ji Xiao-xing Peng State Key Laboratory of Water Resources and Hydropower Engineering Science Wuhan UniversityWuhan430072China National Key Laboratory on Ship Vibration and Noise China Ship Scientific Research CenterWuxi214082China

In this paper, the large eddy simulation (LES) method in conjunction with the Zwart cavitation model is adopted for the assessment of the erosion risk on a hydrofoil surface. The numerical results are in good agreement with the experiments. On this basis, three methods, namely the intensity function method (IFM), the time-averaged aggressiveness indicators (TAIs) and the gray level method (GLM), are applied for the assessment of the erosion risk. It is shown that the erosion intensity index of the IFM is extremely sensitive to the artificially selected thresholds, which greatly limits the application of the method. The erosion risk predicted by four indicators in the TAIs does not agree well with the experimental results. Further analysis demonstrates that the GLM using the instantaneous pressure field is relatively satisfactory, which can provide a reasonable assessment of the erosion and is not very sensitive to the artificially selected thresholds. To further improve the accuracy of the GLM for the erosion risk prediction, the time-average pressure field is adopted in the GLM for the erosion evaluation. It is suggested that the erosion assessment by using the time-averaged pressure field is in better agreement with the experimental results when compared with that by using the instantaneous pressure field.

关键词： Cavitating flow erosion numerical simulation threshold selection

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Adaptive spatial neighborhood analysis and Rayleigh-Gauss distribution fitting for change detection in multi-temporal remote sensing images

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遥感学报 2009年第4期13卷 631-638页

作者： WANG Gui-ting, WANG You-liang, JIAO Li-cheng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Shaanxi Xi’an 710071, China 西安电子科技大学智能信息处理研究所智能感知与图像理解教育部重点实验室陕西西安 710071

This paper proposes a novel automatic change detection approach for single band multi-temporal remote sensing images (MTRSI). First, the difference image is constructed by combining the spatial neighborhood information with the improved multiplying transform fusion (MTF) technique, which can well weaken noises and eliminate the effects caused by the registration error of multi-temporal images. In the segmentation processing of the difference image, the distributions of changed and unchanged classes are fitted by Rayleigh-Gauss models (RGM) and the probability densities of changed and unchanged pixels are estimated. Then the optimal change detection threshold is calculated automatically and effectively by the improved Kittler-Illingworth (KI) threshold selection algorithm. Finally, the changed regions are extracted. The experimental results obtained on the simulated MTRSI and the real MTRSI confirmed the effectiveness of the proposed approach. In particular, the results in terms of overall error and overall detected accuracy proved that the proposed generation approach of the difference image could have better performance than the MTF technique. In addition, as expected, the RGM was proved to be more suitable than the Gauss models (GM) and the Generalized-Gauss models (GGM) to fit the distributions of changed and unchanged classes. And the change detection experiments also confirmed that the proposed automatic threshold selection method based on RGM fitting technique could achieve the very similar performance to the optimal results exhibited by the supervised manual trial and error procedure (MTEP).

关键词： change detection spatial neighborhood analysis Rayleigh-Gauss models threshold selection

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An Improved Double-threshold Method Based on Gradient Histogram

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Wuhan University Journal of Natural Sciences 2004年第4期9卷 473-476页

作者： YANGShen CHENShu-zhen ZHANGBing SchoolofElectronicInformation WuhanUniversityWuhan430072HubeiChina TheSecondEngineeringLimitedCompanyofChinaRailwayElectrificationBureauGroup Xiangfan441003HubeiChina SchoolofElectronicInformation WuhanUniversityWuhan430072HubeiChina

This paper analyzes the characteristics of the output gradient histogram and shortages of several traditional automatic threshold methods in order to segment the gradient image better. Then an improved double-threshold method is proposed, which is combined with the method of maximum classes variance, estimating-area method and double-threshold method. This method can automatically select two different thresholds to segment gradient images. The computer simulation is performed on the traditional methods and this algorithm and proves that this method can get satisfying result. Key words gradient histogram image - threshold selection - double-threshold method - maximum classes variance method CLC number TP 391. 41 Foundation item: Supported by the National Nature Science Foundation of China (50099620) and the Project of Chenguang Plan in Wuhan (985003062)Biography: YANG Shen (1977-), female, Ph. D. candidate, research direction: multimedia information processing and network technology.

关键词： gradient histogram image threshold selection double-threshold method maximum classes variance method

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1956-2010年淮河流域极端径流的时空变化及统计模拟(英文)

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Journal of Geographical Sciences 2012年第6期22卷 1045-1060页

作者：夏军杜鸿曾思栋佘敦先张永勇严子奇叶琰 Key Laboratory of Water Cycle and Related land Surface Processes Institute of Geographic Sciences and Natural Resources ResearchCAS State key laboratory of Water Resources and Hydropower Engineering Science Wuhan University Graduate University of Chinese Academy of Sciences China Institute of Water Resources and Hydropower Research College of Natural Resources and Environment Southwest University College of Water Resources and Hydropower Sichuan University

Based on the daily runoff data from 20 hydrological stations above the Bengbu Sluice in the Huaihe River Basin during 1956-2010, run test, trend test and Mann-Kendall test are used to analyze the variation trend of annual maximum runoff series. The annual maximum series （AM） and peaks over threshold series （POT） are selected to describe the extreme distributions of generalized extreme value distribution （GEV） and generalized Pareto distri- bution （GPD）. Temporal and spatial variations of extreme runoff in the Huaihe River Basin are analyzed. The results show that during the period 1956-2010 in the Huaihe River Basin, annual maximum runoff at 10 stations have a decreasing trend, while the other 10 stations have an unobvious increasing trend. The maximum runoff events almost occurred in the flood period during the 1960s and 1970s. The extreme runoff events in the Huaihe River Basin mainly occurred in the mainstream of the Huaihe River, Huainan mountainous areas, and Funiu mountainous areas. Through Kolmogorov-Smirnov test, GEV and GPD distributions can be well fitted with AM and POT series respectively. Percentile value method, mean ex- cess plot method and certain numbers of peaks over threshold method are used to select threshold, and it is found that percentile value method is the best of all for extreme runoff in the Huaihe River Basin.

关键词： Huaihe River extreme runoff extreme distribution threshold selection

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基于混沌PSO或分解的二维Tsallis交叉熵阈值分割(英文)

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China Communications 2011年第7期8卷 111-121页

作者：吴一全张晓杰吴诗婳 College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics State Key Laboratory for Novel Software Technology Nanjing University

The segmentation effect of Tsallis entropy method is superior to that of Shannon entropy method, and the computation speed of two-dimensional Shannon cross entropy method can be further improved by optimization. The existing two-dimensional Tsallis cross entropy method is not the strict two-dimensional extension. Thus two new methods of image thresholding using two-dimensional Tsallis cross entropy based on either Chaotic Particle Swarm Optimization (CPSO) or decomposition are proposed. The former uses CPSO to find the optimal threshold. The recursive algorithm is adopted to avoid the repetitive computation of fitness function in iterative procedure. The computing speed is improved greatly. The latter converts the two-dimensional computation into two one-dimensional spaces, which makes the computational complexity further reduced from O(L2) to O(L). The experimental results show that, compared with the proposed recently two-dimensional Shannon or Tsallis cross entropy method, the two new methods can achieve superior segmentation results and reduce running time greatly.

关键词： signal and information processing image segmentation threshold selection two-dimensional Tsallis cross entropy chaotic particle swarm optimization decomposition

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