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The Asymptotic Behavior of the Optimal Threshold Minimizing the Probability-of-Error Criterion

Journal of Mathematical Sciences (United States) 2018年第6期234卷 810-815页

作者： Kudryavtsev, A.A. Shestakov, O.V. Department of Mathematical Statistics Faculty of Computational Mathematics and Cybernetics Lomonosov Moscow State University Moscow Russian Federation Institute of Informatics Problems Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences Moscow Russian Federation

In this paper we consider the problem of estimation of a signal function from the noised observations via thresholding its wavelet coefficients. We find the asymptotic order of the optimal threshold that minimizes the probability of the maximum error between the estimates and the true wavelet coefficients exceeding a critical value. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

关键词： ASYMPTOTIC distribution thresholding algorithms ERROR analysis (Mathematics) WAVELETS (Mathematics) SET theory

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Image thresholding Improved by Global Optimization Methods

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APPLIED ARTIFICIAL INTELLIGENCE 2017年第3期31卷 197-208页

作者： Balabanian, Felipe Sant'Ana da Silva, Eduardo Pedrini, Helio Univ Estadual Campinas Inst Comp Ave Albert Einstein 1251 BR-13083852 Campinas SP Brazil

Image thresholding is a common segmentation technique with applications in various fields, such as computer vision, pattern recognition, microscopy, remote sensing, and biology. The selection of threshold values for segmenting pixels into foreground and background regions is usually based on subjective assumptions or user judgments under empirical rules or manually determined. This work describes and evaluates six effective threshold selection strategies for image segmentation based on global optimization methods: genetic algorithms, particle swarm, simulated annealing, and pattern search. Experiments are conducted on several images to demonstrate the effectiveness of the proposed methodology.

关键词： Segmentation (Image processing) thresholding algorithms Global optimization Computer vision Pattern perception Remote sensing

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Consistency-based thresholding of the human connectome

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NEUROIMAGE 2017年第PartA期145卷 118-129页

作者： Roberts, James A. Perry, Alistair Roberts, Gloria Mitchell, Philip B. Breakspear, Michael QIMR Berghofer Med Res Inst Syst Neurosci Grp Herston Qld 4006 Australia QIMR Berghofer Med Res Inst Ctr Integrat Brain Funct Herston Qld 4006 Australia Univ New South Wales Sch Psychiat Ctr Hlth Brain Ageing CHeBA Sydney NSW Australia Univ New South Wales Sch Psychiat Sydney NSW 2052 Australia Prince Wales Hosp Black Dog Inst Hosp Rd Randwick NSW 2031 Australia Royal Brisbane & Womens Hosp Metro North Mental Hlth Serv Brisbane Qld Australia

Densely seeded probabilistic tractography yields weighted networks that are nearly fully connected, hence containing many spurious fibers. It is thus necessary to prune spurious connections from probabilistically derived networks to obtain a more reliable overall estimate of the connectivity. A standard method is to threshold by weight, keeping only the strongest edges. Here, by measuring the consistency of edge weights across subjects, we propose a new thresholding method that aims to reduce the rate of false-positives in group averaged connectivity matrices. Close inspection of the relationship between consistency, weight, and distance suggests that the most consistent edges are in fact those that are strong for their length, rather than simply strong overall. Hence retaining the most consistent edges preserves more long-distance connections than traditional weight-based thresholding, which penalizes long connections for being weak regardless of anatomy. By comparing our thresholded networks to mouse and macaque tracer data, we also show that consistency based thresholding exhibits the species-invariant exponential decay of connection weights with distance, while weight-based thresholding does not. We also show that consistency-based thresholding can be used to identify highly consistent and highly inconsistent subnetworks across subjects, enabling more nuanced analyses of group-level connectivity than just the mean connectivity.

关键词： BRAIN mapping NEURAL circuitry BRAIN anatomy BRAIN weight thresholding algorithms

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Automatic thresholding Method for Edge Detection algorithms 8

Automatic Thresholding Method for Edge Detection Algorithms

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8th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)

作者： Anghelescu, Petre Iliescu, Vlad Gabriel Mara, Constantin Gavriloaia, Mihai-Bogdan Univ Pitesti Dept Elect Commun & Comp Pitesti Romania Univ Politehn Bucuresti Telecommun Dpt Elect Bucharest Romania

ISBN: (纸本)9781509020478

This paper describes an efficient edge detection algorithm that can be used as a plug-in for digital image processing systems. The proposed algorithm uses a method based on iterative clustering targeting a reduced number of operations. The algorithm splits the image into two parts, background and foreground, and calculates the mean value for each of them. Based on these results, the new threshold value will be obtained and looped until the mean values remain unchanged. The only pixels affected by the change are the pixels with values between the previous two thresholds, so only they have to be redistributed to a new class. As a result, only few operations are needed in order to obtain the desired threshold. All the algorithms and results obtained in this paper are developed and tested using the C# programming language.

关键词： image processing edge detection thresholding algorithms non-maximum suppression cluster based methods

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An Intelligent Grey Wolf Optimizer Algorithm for Distributed Compressed Sensing

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2018年第1期2018卷 1723191-1723191页

作者： Liu, Haiqiang Hua, Gang Yin, Hongsheng Xu, Yonggang China Univ Min & Technol Xuzhou 221116 Peoples R China

Distributed Compressed Sensing (DCS) is an important research area of compressed sensing (CS). This paper aims at solving the Distributed Compressed Sensing (DCS) problem based on mixed support model. In solving this problem, the previous proposed greedy pursuit algorithms easily fall into suboptimal solutions. In this paper, an intelligent grey wolf optimizer (GWO) algorithm called DCS-GWO is proposed by combining GWO and q-thresholding algorithm. In DCS-GWO, the grey wolves' positions are initialized by using the q-thresholding algorithm and updated by using the idea of GWO. Inheriting the global search ability of GWO, DCS-GWO is efficient in finding global optimum solution. The simulation results illustrate that DCS-GWO has better recovery performance than previous greedy pursuit algorithms at the expense of computational complexity.

关键词： COMPRESSED sensing (Signal processing) thresholding algorithms GREEDY algorithms DISTRIBUTED computing COMPUTATIONAL complexity PROBLEM solving

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An investigation of image processing techniques for substrate classification based on dominant grain size using RGB images from UAV

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2017年第8-10期38卷 2639-2661页

作者： Arif, Mohammad Shafi M. Guelch, Eberhard Tuhtan, Jeffrey A. Thumser, Philipp Haas, Christian Hess Landgesellschaft mbH Flachenmanagement Strassenbau Giessen Germany Stuttgart Univ Appl Sci Fac Surveying Comp Sci & Math Stuttgart Germany Tallinn Univ Technol Ctr Biorobot Tallinn Estonia HYDRO GmbH St Georgen Germany

Imagery collected with an unmanned aerial vehicle (UAV) in conjunction with image processing provides new sources of environmental intelligence data and can be implemented in river habitat studies. High-resolution RGB orthomosaic images with 1 cm/px resolution are generated from RGB images acquired with a UAV. Ground truth mapping of the dominant substrate of the river bottom is then used to classify each spatial region. Several texture parameters are examined using image processing techniques to determine the presence and extent of each of the dominant grain classes, providing a method to classify and map the river bed. The method differentiates between submerged, dry exposed, and vegetated regions. The image cover was classified via application and examination of a variety of pixel-based image classification methods. The highest classification accuracy for pixel based analysis was achieved using the thresholding and masking algorithm which achieved an overall 97% correct classification. In addition, object-based image classification was applied using different grey-level co-occurrence matrices (GLCM) in all directions. The classification accuracy for segmentation based classification was found to be lower, at 61%.

关键词： DRONE aircraft thresholding algorithms TEXTURE analysis (Image processing) GRAIN size SEGMENTATION (Image processing) HIGH resolution imaging

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A New Change Detector in Heterogeneous Remote Sensing Imagery 7

A New Change Detector in Heterogeneous Remote Sensing Imager...

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7th International Conference on Image Processing Theory, Tools and Applications (IPTA)

作者： Touati, Redha Mignotte, Max Dahmane, Mohamed Univ Montreal DIRO Montreal PQ Canada CRIM R&D Vis Dept Montreal PQ Canada

ISBN: (纸本)9781538618424

Multimodal change detection in satellite images is a challenging and complex problem mainly because the local statistics of the images to be compared can be very different. In this paper, we present a novel, reliable and simple change detection operator which is first based on a imaging modality-invariant operator that detects the common specific high-frequency pattern of each structural region existing in the two heterogeneous satellite images. The resultant similarity map is then filtered out by a superpixel-based spatially adaptive filter which increases its reliability against noise. Second, in order to achieve more robustness, changes are then identified, from this similarity map, by combining the results of different automatic thresholding algorithms with a weighted spatially regularized multi-criteria decision analysis. Experimental results involving a mixture of different types of imaging modalities confirm the robustness of the proposed approach.

关键词： Change detection Data fusion High-frequency based operator Multitemporal multimodal images Spatial-temporal-based CD operator thresholding algorithms

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Modified Discrete Grey Wolf Optimizer Algorithm for Multilevel Image thresholding

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2017年第20170101期2017卷 3295769-3295769页

作者： Li, Linguo Sun, Lijuan Guo, Jian Qi, Jin Xu, Bin Li, Shujing Nanjing Univ Posts & Telecommun Sch Comp Nanjing 210003 Jiangsu Peoples R China Fuyang Normal Univ Coll Informat Engn Fuyang 236041 Peoples R China Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing 210003 Jiangsu Peoples R China

The computation of image segmentation has become more complicated with the increasing number of thresholds, and the option and application of the thresholds in image thresholding fields have become an NP problem at the same time. The paper puts forward the modified discrete grey wolf optimizer algorithm (MDGWO), which improves on the optimal solution updating mechanism of the search agent by the weights. Taking Kapur's entropy as the optimized function and based on the discreteness of threshold in image segmentation, the paper firstly discretizes the grey wolf optimizer (GWO) and then proposes a new attack strategy by using the weight coefficient to replace the search formula for optimal solution used in the original algorithm. The experimental results show that MDGWO can search out the optimal thresholds efficiently and precisely, which are very close to the result examined by exhaustive searches. In comparison with the electromagnetism optimization (EMO), the differential evolution (DE), the Artifical Bee Colony (ABC), and the classical GWO, it is concluded that MDGWO has advantages over the latter four in terms of image segmentation quality and objective function values and their stability.

关键词： SEGMENTATION (Image processing) thresholding algorithms ENTROPY (Information theory) ELECTROMAGNETISM DIFFERENTIAL evolution (Computer science)

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Improved Glowworm Swarm Optimization Algorithm for Multilevel Color Image thresholding Problem

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MATHEMATICAL PROBLEMS IN ENGINEERING 2016年第1期2016卷 1-24页

作者： He, Lifang Huang, Songwei Kunming Univ Sci & Technol Dept Elect & Commun Engn Kunming 650093 Peoples R China Kunming Univ Sci & Technol Dept Mineral Proc Kunming 650093 Peoples R China

The thresholding process finds the proper threshold values by optimizing a criterion, which can be considered as a constrained optimization problem. The computation time of traditional thresholding techniques will increase dramatically for multilevel thresholding. To greatly overcome this problem, swarm intelligence algorithm is widely used to search optimal thresholds. In this paper, an improved glowwormswarmoptimization (IGSO) algorithmhas been presented to find the optimalmultilevel thresholds of color image based on the between-class variance and minimum cross entropy (MCE). The proposed methods are examined on standard set of color test images by using various numbers of threshold values. The results are then compared with those of basic glowworm swarm optimization, adaptive particle swarm optimization (APSO), and self-adaptive differential evolution (SaDE). The simulation results show that the proposed method can find the optimal thresholds accurately and efficiently and is an effective multilevel thresholding method for color image segmentation.

关键词： PARTICLE swarm optimization COLOR image processing thresholding algorithms ADAPTIVE testing DIFFERENTIAL evolution (Computer science) COMPUTER simulation SEGMENTATION (Image processing)

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Approximately Normalized Iterative Hard thresholding for Nonlinear Compressive Sensing

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MATHEMATICAL PROBLEMS IN ENGINEERING 2016年 2016卷 1-8页

作者： Zhu, Xunzhi Shandong Univ Technol Sch Sci Zibo 255000 Peoples R China

The nonlinear compressive sensing (NCS) is an extension of classical compressive sensing (CS) and the iterative hard thresholding (IHT) algorithm is a popular greedy-type method for solving CS. The normalized iterative hard thresholding (NIHT) is a modification of IHT and is more effective than IHT. In this paper, we propose an approximately normalized iterative hard thresholding (ANIHT) algorithm for NCS by using the approximate optimal stepsize combining with Armijo stepsize rule preiteration. Under the condition similar to restricted isometry property (RIP), we analyze the condition that can identify the iterative support sets in a finite number of iterations. Numerical experiments show the good performance of the new algorithm for the NCS.

关键词： ITERATIVE methods (Mathematics) thresholding algorithms COMPRESSED sensing (Signal processing) GREEDY algorithms RESTRICTED isometry property

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