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IEEE OPEN JOURNAL OF SIGNAL PROCESSING 2022年 3卷 417-431页

作者： Zhao, Yun-Bin Luo, Zhi-Quan Chinese Univ Hong Kong Shenzhen Res Inst Big Data Shenzhen 518172 Guangdong Peoples R China

The algorithms based on the technique of optimal k-thresholding (OT) were recently proposed for signal recovery, and they are very different from the traditional family of hard thresholding methods. However, the computational cost for OT-based algorithms remains high at the current stage of their development. This stimulates the development of the so-called natural thresholding (NT) algorithm and its variants in this paper. The family of NT algorithms is developed through the first-order approximation of the so-called regularized optimal k-thresholding model, and thus the computational cost for this family of algorithms is significantly lower than that of the OT-based algorithms. The guaranteed performance of NT-type algorithms for signal recovery from noisy measurements is shown under the restricted isometry property and concavity of the objective function of regularized optimal k-thresholding model. Empirical results indicate that the NTtype algorithms are robust and very comparable to several mainstream algorithms for sparse signal recovery.

关键词： Compressed sensing concave minimization regularization method restricted isometry property signal recovery thresholding algorithms

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HARDWARE IMPLEMENTATION OF ISODATA AND OTSU thresholding algorithms 21

HARDWARE IMPLEMENTATION OF ISODATA AND OTSU THRESHOLDING ALG...

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21st Symposium on Signal Processing, Images and Artificial Vision (STSIVA)

作者： Torres-Monsalve, Albert F. Velasco-Medina, Jaime Univ Valle Bionanoelect Res Grp Sch Elect & Elect Engn Cali Colombia

ISBN: (纸本)9781509037971

Image and video processing algorithms implemented in software, require most computation time when the image size is increased. Also, some algorithms must be processed at high-speed, for example the image thresholding algorithms for high throughput real-time applications. Then, in order to overcome this requirement, the algorithms must be efficiently implemented in hardware. In this paper, we present the hardware architectures for ISODATA and Otsu thresholding algorithms comparing area, latency, throughput and power consumption. The designs are described using generic structural VHDL and synthesized on the FPGA EP4CE115F29C7N. The designed architectures were verified using Signal Tap and an image acquisition system based on the D5M camera and the DE2-115 development kit of Terasic.

关键词： Computer Vision Video Processing Otsu ISODATA thresholding algorithms

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A Comparative Study of Improved Artificial Bee Colony algorithms Applied to Multilevel Image thresholding

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MATHEMATICAL PROBLEMS IN ENGINEERING 2013年第1期2013卷 1-17页

作者： Charansiriphaisan, Kanjana Chiewchanwattana, Sirapat Sunat, Khamron Khon Kaen Univ Fac Sci Dept Comp Sci Khon Kaen 40002 Thailand

Multilevel thresholding is a highly useful tool for the application of image segmentation. Otsu's method, a common exhaustive search for finding optimal thresholds, involves a high computational cost. There has been a lot of recent research into various metaheuristic searches in the area of optimization research. This paper analyses and discusses using a family of artificial bee colony algorithms, namely, the standard ABC, ABC/best/1, ABC/best/2, IABC/best/1, IABC/rand/1, and CABC, and some particle swarm optimization-based algorithms for searching multilevel thresholding. The strategy for an onlooker bee to select an employee bee was modified to serve our purposes. The metric measures, which are used to compare the algorithms, are the maximum number of function calls, successful rate, and successful performance. The ranking was performed by Friedman ranks. The experimental results showed that IABC/best/1 outperformed the other techniques when all of them were applied to multilevel image thresholding. Furthermore, the experiments confirmed that IABC/best/1 is a simple, general, and high performance algorithm.

关键词： BEES algorithm SEGMENTATION (Image processing) thresholding algorithms MATHEMATICAL optimization PARTICLE swarm optimization METAHEURISTIC 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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Sparse Recovery by Semi-Iterative Hard thresholding Algorithm

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MATHEMATICAL PROBLEMS IN ENGINEERING 2013年第1期2013卷 1-6页

作者： Zhou, Xueqin Feng, Xiangchu Jing, Mingli Xidian Univ Sch Sci Xian 710071 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Shaanxi Peoples R China Xian Univ Finance & Econ Sch Stat Xian 710100 Shaanxi Peoples R China

We propose a computationally simple and efficient method for sparse recovery termed as the semi-iterative hard thresholding (SIHT). Unlike the existing iterative-shrinkage algorithms, which rely crucially on using negative gradient as the search direction, the proposed algorithm uses the linear combination of the current gradient and directions of few previous steps as the search direction. Compared to other iterative shrinkage algorithms, the performances of the proposed method show a clear improvement in iterations and error in noiseless, whilst the computational complexity does not increase.

关键词： SPARSE matrices ITERATIVE methods (Mathematics) thresholding algorithms SIGNAL processing COMPUTATIONAL complexity ELECTRONIC data processing

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Model-Based Adaptive Iterative Hard thresholding Compressive Sensing in Sensor Network for Volcanic Earthquake Detection

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2015年第7期2015卷 1-8页

作者： Liu, Guojin Zhang, Qian Yang, Yuyuan Yin, Zhenzhi Zhu, Bin Chongqing Univ Coll Commun Engn Chongqing 400030 Peoples R China

Recent years have witnessed pilot deployments of inexpensive wireless sensor networks (WSNs) for volcanic eruption detection, where the volcano-seismic signals were collected and processed by sensor nodes. However, it is faced with the limitation of energy resources and the transmission bottleneck of sensors in WSN. In this paper, a Model-Based Adaptive Iterative Hard thresholding (MAIHT) compressive sensing scheme is developed, where a large number of inexpensive sensors are used to collect fine-grained, real-time volcano-seismic signals while a small number of powerful coordinator nodes process and pick arrival times of primary waves (i.e., P-phases). The paper contribution is two-fold. Firstly, a sparse measurement matrix with theoretical analysis of its restricted isometry property (RIP) is designed to simplify the acquisition process, thereby reducing required storage space and computational demands in sensors. Secondly, a compressive sensing reconstruction algorithm with theoretical analysis of its error bound is presented. Experimental results based on real volcano-seismic data collected from a volcano show that our method can recover the original seismic signal and achieve accurate P-phase picking based on the reconstructed seismic signal.

关键词： WIRELESS sensor networks thresholding algorithms EARTHQUAKES COMPRESSED sensing (Signal processing) VOLCANIC eruptions IMAGE reconstruction algorithms MATHEMATICAL models WIRELESS sensor nodes

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A New Wavelet thresholding Function Based on Hyperbolic Tangent Function

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MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-10页

作者： He, Can Xing, Jianchun Li, Juelong Yang, Qiliang Wang, Ronghao PLA Univ Sci & Technol Coll Def Engn Nanjing 210007 Jiangsu Peoples R China Naval Def Engn Tech Management Off Beijing 100841 Peoples R China

thresholding function is an important part of the wavelet threshold denoising method, which can influence the signal denoising effect significantly. However, some defects are present in the existing methods, such as function discontinuity, fixed bias, and parameters determined by trial and error. In order to solve these problems, a new wavelet thresholding function based on hyperbolic tangent function is proposed in this paper. Firstly, the basic properties of hyperbolic tangent function are analyzed. Then, a new thresholding function with a shape parameter is presented based on hyperbolic tangent function. The continuity, monotonicity, and high-order differentiability of the new function are theoretically proven. Finally, in order to determine the final form of the new function, a shape parameter optimization strategy based on artificial fish swarm algorithm is given in this paper. Mean square error is adopted to construct the objective function, and the optimal shape parameter is achieved by iterative search. At the end of the paper, a simulation experiment is provided to verify the effectiveness of the new function. In the experiment, two benchmark signals are used as test signals. Simulation results show that the proposed function can achieve better denoising effect than the classical hard and soft thresholding functions under different signal types and noise intensities.

关键词： WAVELETS (Mathematics) FUNCTIONS (Mathematics) thresholding algorithms HYPERBOLIC functions SIGNAL denoising

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Comparison of automatic thresholding methods for snow-cover mapping using Landsat TM imagery

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2013年第19期34卷 6529-6538页

作者： Yin, Dameng Cao, Xin Chen, Xuehong Shao, Yijie Chen, Jin Beijing Normal Univ State Key Lab Earth Surface Proc & Resource Ecol Beijing 100875 Peoples R China

Knowledge of snow cover is essential to understanding the global water and energy cycle. thresholding the normalized difference snow index (NDSI) image is a method frequently used to map snow cover from remotely sensed data. However, the threshold is dependent on the scenario and needs to be determined accordingly. In this study, nine automatic thresholding methods were tested on the NDSI. Comparisons of the automatic thresholding methods, optimal threshold, and support vector machine (SVM) classification show that Otsu's and Nie's methods appear to be the most robust among the nine automatic thresholding methods, achieving comparable accuracies with the latter two approaches. In addition, NDSI from the digital number (DN) can be an efficient substitution for NDSI obtained from atmospherically or topographically corrected data, with similar accuracy.

关键词： SNOW cover LANDSAT satellites thresholding algorithms REMOTE sensing SUPPORT vector machines COMPARATIVE studies

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Automatic thresholding Technique Using Reconfigurable Hardware

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CYBERNETICS AND SYSTEMS 2021年第2期52卷 145-168页

作者： Anghelescu, Petre Univ Pitesti Dept Elect Commun & Comp Pitesti 0300 Arges Romania

This research presents an efficient automatic thresholding technique based on Otsu's method that can be used in edge detection algorithms and then applied as a plug-in for real-time image processing devices. The proposed thresholding technique uses an iterative clustering based method that targets a reduced number of operations. It is well known that the Otsu calculates the global threshold splitting the image into two classes, foreground and background, and choose the threshold that minimizes the interclass variance of the threshold black and white pixels. In this paper, a faster version of Otsu's method is proposed knowing that the only pixels that have to be moved from one class to another class are the ones with values in between the previous two thresholds. This procedure yields the same set of thresholds as the original method but the redundant computation has been removed and, in this way, only few operations are required. The proposed thresholding technique has been implemented in software using C# programing language and in reconfigurable hardware on a Spartan 3E XC3S500E FPGA board using VHDL. The results obtained, presented for different digital images, confirm that the proposed iterative thresholding algorithm and architecture on FPGA can achieve the requirements to be included in real-time image processing systems.

关键词： Cluster based methods edge detection FPGA image binarization Otsu's method thresholding algorithms

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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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