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Fast Texture Synthesis for Discrete Example-Based Elements

IEEE ACCESS 2020年 8卷 76683-76691页

作者： Huang, Zhengrui Lin, Xiaohong Chen, Chongcheng Fuzhou Univ Key Lab Spatial Data Min & Informat Sharing Minist Educ Fuzhou 350108 Peoples R China Natl Engn Res Ctr Geospatial Informat Technol Fuzhou 350108 Peoples R China Fuzhou Technol & Business Coll Fuzhou 350007 Peoples R China

Considering the problem of discrete texture synthesis and the time for texturing, this paper proposes a novel framework for synthesizing texture images based on discrete example-based elements. We start with extracting texture feature distribution from exemplars and then produce discrete elements based on the cluster algorithm. After initializing a texture image, we propose a texture optimization algorithm based on heuristic searching to improve the quality of the texture image. Final, we use a texture transfer method based on Convolutional Neural Network (CNN) to stylize the optimized texture image. Our results show that the proposed texture synthesis method can significantly improve the quality of discrete texture synthesis and effectively shorten the time for texture generation.

关键词： Texture synthesis discrete elements cluster algorithm heuristic searching CNN

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Adaptive in-focus particle detection and segmentation in holographic 3D image with mechanism-guided machine learning

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OPTICS AND LASERS IN ENGINEERING 2024年 181卷

作者： Zhang, Hang Wang, Boyi Zhang, Letian Zhao, Yue Wang, Yu Feng, Jianhan Xiao, Wei Wang, Gaofeng Wu, Yingchun Wu, Xuecheng Zhejiang Univ State Key Lab Clean Energy Utilizat Hangzhou 310027 Peoples R China Zhejiang Univ Sch Aeronaut & Astronaut Hangzhou 310027 Peoples R China AECC Aviat Powerplant Res Inst Zhuzhou 412000 Hunan Peoples R China Zhejiang Univ Huzhou Inst Huzhou 313000 Peoples R China

In the domain of three-dimensional (3D) particle holography, diverse imaging characteristics across particles of varying scales and positions pose a notable impediment to efficient information extraction. This research introduces an adaptive detection and segmentation approach employing mechanism-guided machine learning techniques. Not only the carefully chosen detection network but also the cropping pretreatment of input images are used to obtain the precise regions of interest (ROIs) of particles, inspired with the estimated particle size distribution under specific experimental conditions. Restricted by the blurred particle boundary and aiming for a robust method for hyperparameter, cluster-based segmentation is proposed with an extra feature map, which takes the unique change of object signals across different slices compared with background into account. Rigorous evaluation of different detection network configurations, training data, and segmentation methods are conducted on experimental data from an icing wind tunnel and calibration dot board. Concrete ablation experiments and analysis of the mechanism-guided operations (cropping and the introduction of the extra feature map) are provided. The influence of the hyperparameter in segmentation is also illustrated. To showcase its robustness, the proposed method is applied to segment a swirl spray particle field, demonstrating its versatility and applicability across varied particle field scenarios. The code and data for the paper are available at https://github .com /Adiazhang0426 /HOLOAI /tree /master and https://drive .google .com /drive / folders /1C9QlaklXpApIpE8AukCNMTKUnZw _YcGJ ?usp =drive _link.

关键词： Particle holography 3D image segmentation Detection network cluster algorithm Mechanism-guided method

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Logarithmic corrections and finite-size scaling in the two-dimensional 4-state Potts model

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JOURNAL OF STATISTICAL PHYSICS 1997年第3-4期88卷 567-615页

作者： Salas, J Sokal, AD NYU DEPT PHYS NEW YORK NY 10003 USA

We analyze the scaling and finite-size-scaling behavior of the two-dimensional 4-state Potts model. We find new multiplicative logarithmic corrections for the susceptibility, in addition to the already known ones for the specific heat. We also find additive logarithmic corrections to scaling, some of which are universal. We have checked the theoretical predictions at criticality and off criticality by means of high-precision Monte Carlo data.

关键词： scaling finite-size scaling critical phenomena renormalization group logarithmic corrections corrections to scaling Potts model Ashkin-Teller model Swendsen-Wang algorithm cluster algorithm Monte Carlo

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A new concept of Cosine similarity measures based on dual hesitant fuzzy sets and its possible applications

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cluster COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第6-Sup期22卷 15483-15492页

作者： Zhang, Yutao Wang, Lei Yu, Xiaohan Yao, Changhua Nanjing Univ Sci & Technol Coll Econ & Management Nanjing 210094 Jiangsu Peoples R China Nanjing Gen Hosp Postdoctoral Res Ctr Nanjing 210002 Jiangsu Peoples R China Army Engn Univ PLA Coll Commun Engn Nanjing 210007 Jiangsu Peoples R China Army Engn Univ PLA Coll Command & Control Engn Nanjing 210007 Jiangsu Peoples R China

Most real-world problems are typical multi-criteria decision making problems which including ambiguity and subjectivity. Dual fuzzy sets (DHFS) are new extensions of fuzzy sets, which can cope with areas of vagueness effectively. In this paper, we propose one new similarity measure called cosine measure which has the ability to model various problems for DHFSs and study some formal relations. We provided three numerical examples including a medical diagnosis problem, an energy

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An efficient data collection and load balance algorithm in wireless sensor networks

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WIRELESS NETWORKS 2019年第7期25卷 3703-3714页

作者： Qin, Danyang Ji, Ping Yang, Songxiang Berhane, Teklu Merhawit Heilongjiang Univ Harbin Peoples R China Heilongjiang Univ Sch Elect Engn Harbin Peoples R China Dire Dawa Inst Technol Dire Dawa Ethiopia

The nature of multi-hop data transmission in wireless sensor network will cause serious load unbalance which will produce great restrains in related applications considering the limited energy resource. Relative load balance algorithms are usually performed inside the clusters without considering about the energy consumption of the whole network. A cluster-based balanced energy consumption algorithm (BECA) is proposed by introducing in multiple inter-cluster links to distribute the load, so as to achieve global load balance. Moreover, an efficient data collecting mechanism is proposed based on BECA to improve the traffic balance further. Simulating results based on NS2 show that BECA can obtain better balance properties and prolong the network lifetime effectively.

关键词： Wireless sensor network Data collection Load balance cluster algorithm Multiple links Lifetime

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PFClust: a novel parameter free clustering algorithm

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BMC BIOINFORMATICS 2013年第1期14卷 1-21页

作者： Mavridis, Lazaros Nath, Neetika Mitchell, John B. O. Univ St Andrews St Andrews KY16 9ST Fife Scotland Univ St Andrews EaStCHEM Sch Chem St Andrews KY16 9ST Fife Scotland

Background: We present the algorithm PFClust (Parameter Free clustering), which is able automatically to cluster data and identify a suitable number of clusters to group them into without requiring any parameters to be specified by the user. The algorithm partitions a dataset into a number of clusters that share some common attributes, such as their minimum expectation value and variance of intra-cluster similarity. A set of n objects can be clustered into any number of clusters from one to n, and there are many different hierarchical and partitional, agglomerative and divisive, clustering methodologies available that can be used to do this. Nonetheless, automatically determining the number of clusters present in a dataset constitutes a significant challenge for clustering algorithms. Identifying a putative optimum number of clusters to group the objects into involves computing and evaluating a range of clusterings with different numbers of clusters. However, there is no agreed or unique definition of optimum in this context. Thus, we test PFClust on datasets for which an external gold standard of 'correct' cluster definitions exists, noting that this division into clusters may be suboptimal according to other reasonable criteria. PFClust is heuristic in the sense that it cannot be described in terms of optimising any single simply-expressed metric over the space of possible clusterings. Results: We validate PFClust firstly with reference to a number of synthetic datasets consisting of 2D vectors, showing that its clustering performance is at least equal to that of six other leading methodologies - even though five of the other methods are told in advance how many clusters to use. We also demonstrate the ability of PFClust to classify the three dimensional structures of protein domains, using a set of folds taken from the structural bioinformatics database CATH. Conclusions: We show that PFClust is able to cluster the test datasets a little better, on average, than

关键词： cluster algorithm Synthetic Dataset Average Similarity Rand Index Original cluster

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Reproducible clusters from microarray research: Whither?

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BMC BIOINFORMATICS 2005年第s2期6卷 S10-S10页

作者： Garge, NR Page, GP Sprague, AP Gorman, BS Allison, DB Univ Alabama Birmingham Dept Biostat Sect Stat Genet Birmingham AL 35294 USA Univ Alabama Birmingham Dept Comp & Informat Sci Birmingham AL 35294 USA Hofstra Univ Dept Psychol Hempstead NY 11550 USA Med Coll Georgia Augusta GA 30912 USA

Motivation: In cluster analysis, the validity of specific solutions, algorithms, and procedures present significant challenges because there is no null hypothesis to test and no 'right answer'. It has been noted that a replicable classification is not necessarily a useful one, but a useful one that characterizes some aspect of the population must be replicable. By replicable we mean reproducible across multiple samplings from the same population. Methodologists have suggested that the validity of clustering methods should be based on classifications that yield reproducible findings beyond chance levels. We used this approach to determine the performance of commonly used clustering algorithms and the degree of replicability achieved using several microarray datasets. Methods: We considered four commonly used iterative partitioning algorithms ( Self Organizing Maps (SOM), K-means, Clutsering LARge Applications (CLARA), and Fuzzy C-means) and evaluated their performances on 37 microarray datasets, with sample sizes ranging from 12 to 172. We assessed reproducibility of the clustering algorithm by measuring the strength of relationship between clustering outputs of subsamples of 37 datasets. cluster stability was quantified using Cramer's v(2) from a kXk table. Cramer's v(2) is equivalent to the squared canonical correlation coefficient between two sets of nominal variables. Potential scores range from 0 to 1, with 1 denoting perfect reproducibility. Results: All four clustering routines show increased stability with larger sample sizes. K-means and SOM showed a gradual increase in stability with increasing sample size. CLARA and Fuzzy C-means, however, yielded low stability scores until sample sizes approached 30 and then gradually increased thereafter. Average stability never exceeded 0.55 for the four clustering routines, even at a sample size of 50. These findings suggest several plausible scenarios: ( 1) microarray datasets lack natural clustering structure thereby

关键词： cluster algorithm Real Dataset cluster Solution Simulated Dataset Microarray Dataset

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borealis-A generalized global update algorithm for Boolean optimization problems

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OPTIMIZATION LETTERS 2020年第8期14卷 2495-2514页

作者： Zhu, Zheng Fang, Chao Katzgraber, Helmut G. Texas A&M Univ Dept Phys & Astron College Stn TX 77843 USA Microsoft Microsoft Quantum Redmond WA 98052 USA

Optimization problems with Boolean variables that fall into the nondeterministic polynomial (NP) class when cast as decision problems are of fundamental importance in computer science, mathematics, physics and industrial applications. Most notably, solving constraint-satisfaction problems, which are related to spin-glass-like Hamiltonians in physics, remains a difficult numerical task. As such, there has been great interest in designing efficient heuristics to solve these computationally difficult problems. Inspired by parallel tempering Monte Carlo in conjunction with the rejection-free isoenergetic cluster algorithm developed for Ising spin glasses, we present a generalized global update optimization heuristic that can be applied to different NP-complete problems with Boolean variables. The global cluster updates allow for a wide-spread sampling of phase space, thus considerably speeding up optimization. By carefully tuning the pseudo-temperature (needed to randomize the configurations) of the problem, we show that the method can efficiently tackle optimization problems with over-constraints or on topologies with a large site percolation threshold. We illustrate the efficiency of the heuristic on paradigmatic optimization problems, such as the maximum satisfiability problem and the vertex cover problem. Because this physics-based algorithm is an algorithm that searches solutions globally, it performs best for random Max-k-SAT instances.

关键词： Optimization Satisfiability Vertex cover Monte Carlo cluster algorithm

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Surface Defect Detection on Optical Devices Based on Microscopic Dark-Field Scattering Imaging

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STROJNISKI VESTNIK-JOURNAL OF MECHANICAL ENGINEERING 2015年第1期61卷 24-32页

作者： Yin, Yingjie Xu, De Zhang, Zhengtao Bai, Mingran Zhang, Feng Tao, Xian Wang, Xingang Chinese Acad Sci Inst Automat Res Ctr Precis Sensing & Control Beijing Peoples R China

Methods of surface defect detection on optical devices are proposed in this paper. First, a series of microscopic dark-field scattering images were collected with a line-scan camera. Translation transformation between overlaps of adjacent microscopic dark-field scattering images resulted from the line-scan camera's imaging feature. An image mosaic algorithm based on scale invariance feature transform (SIFT) is proposed to stitch dark-field images collected by the line-scan camera. SIFT feature matching point-pairs were extracted from regions of interest in the adjacent microscopic dark-field scattering images. The best set of SIFT feature matching point-pairs was obtained via a parallel clustering algorithm: The transformation matrix of the two images was calculated by the best matching point-pair set, and then image stitching was completed through transformation matrix. Secondly, a sample threshold segmentation method was used to segment dark-field images that were previously stitched together because the image background was very dark. Finally, four different supervised learning classifiers are used to classify the defect represented by a six-dimensional feature vector by shape (point or line), and the performance of linear discriminant function (LDF) classifier is demonstrated to be the best. The experimental results showed that defects on optical devices could be detected efficiently by the proposed methods.

关键词： scale invariance feature transform linear discriminant function cluster algorithm image segmentation image mosaic dark-field imaging optical devices

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Dynamic critical behavior of a Swendsen-Wang-type algorithm for the Ashkin-Teller model

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JOURNAL OF STATISTICAL PHYSICS 1996年第3-4期85卷 297-361页

作者： Salas, J Sokal, AD Department of Physics New York University New York

We study the dynamic critical behavior of a Swendsen-Wang-type algorithm for the Ashkin-Teller model. We find that the Li-Sokal bound on the autocorrelation time (tau(int), g greater than or equal to constxC(H)) holds along the self-dual curve of the symmetric Ashkin-Teller model, and is almost, but not quite sharp. The ratio tau(int)epsilon/C-H appears to tend to infinity either as a logarithm or as a small power (0.05 less than or similar to P less than or similar to 0.12). In an appendix we discuss the problem of extracting estimates of the exponential autocorrelation time.

关键词： Ashkin-Teller model Ising model Potts model Monte Carlo dynamical critical behavior cluster algorithm Swendsen-Wang algorithm Li-Sokal bound critical slowing down autocorrelation time, fitting correlated data

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