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Comparative analysis of identification methods for wheat leaf diseases

International Agricultural Engineering Journal 2016年第3期25卷 197-203页

作者： Zhang, D.Y. Xie, X.H. Liang, D. Huang, L.S. Zhang, X.Q. Teng, L. Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China Anhui Engineering Laboratory of Agro-Ecological Big Data Anhui University School of Electronic and Information Engineering Hefei Anhui230601 China

How to fast, accurately and robustly recognize wheat diseases, particularly for those diseases with mild-to-moderate severity, is a challenge for prevention and control of crop disease timely. In this study, image processing technique was applied to segment the infected regions of disease leaves. Twenty disease features were extracted, and eighteen larger weight features were selected by Relief-F algorithm to generate the models of Support Vector Machine (SVM), Relevance Vector Machine (RVM) and Back Propagation Neural Network (BPNN). Subsequently, these models were used to identify two kinds of wheat diseases, namely, wheat stripe rust and powdery mildew. Total 136 samples, including 68 training samples and 68 test samples with different infection severities were used to study the recognition capabilities of the three models. Results showed that high predictive accuracies in identification of two wheat diseases with varying severity for all three models. Overall accuracy of RVM was 89.71%, which was superior to 83.82% of SVM and inferior to 92.64% of BPNN. Meanwhile, the recognition accuracies of SVM, RVM and BPNN models for mild-to-moderate disease were 83.33%, 88.33% and 91.67%, respectively. The prediction time of RVM was less than those of SVM and BPNN, with differences as large as 7.96 and 31.68 times, respectively. Therefore, RVM appeared to be the most suitable for real-time identifying wheat leaf diseases among the three models, which can provide important technical support for wheat diseases management.

关键词： Support vector machines

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An improved reference point sampling method on Pareto optimal front

An improved reference point sampling method on Pareto optima...

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Congress on Evolutionary Computation

作者： Cheng He Linqiang Pan Hang Xu Ye Tian Xingyi Zhang Laboratory of Image Information Processing and Intelligent Control Huazhong University of Science and Technology Wuhan Hubei China Huazhong University of Science and Technology Wuhan Hubei CN Department of Computer Science Xiamen University Xiamen Fujian China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei Anhui China

ISBN: (纸本)9781509006243

In this paper, we propose a sampling approach of reference points used for performance metrics of multi-objective evolutionary algorithms. Traditional reference point sampling methods, such as the Das and Dennis method, usually sample the reference points via a set of uniformly distributed weight vectors generated on an ideal hyper-plane in objective space, which however often ignore the geometric shape of a specific Pareto front. Therefore, we propose a novel reference point sampling approach by taking the specific shape of the Pareto optimal front to be tackled into account for measuring the performance of multi-objective evolutionary algorithms. The performance of the proposed reference point sampling method against the other two state-of-the-art sampling methods is tested on six test instances in various conditions, which clearly demonstrate the effectiveness and superiority of the proposed sampling method.

关键词： Sampling methods Shape Convergence Evolutionary computation Optimization Euclidean distance

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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Two weight Zpk -codes, p odd prime

arXiv

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arXiv 2017年

作者： Shi, Minjia Sepasdar, Zahra Solé, Patrick Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University No. 3 Feixi Road Hefei Anhui Province230039 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China School of Mathematical Sciences of Anhui University Hefei230601 China Department of Pure Mathematics Ferdowsi University of Mashhad P. O. Box 1159 Mashhad91775 Iran CNRS/LTCI Telecom ParisTech University of Paris-Saclay Paris75 013 France Department of Mathematics Faculty of Science King Abdulaziz University Jeddah21589 Saudi Arabia

We show that regular homogeneous two-weight Zpk -codes where p is odd and k ≥ 2 with dual Hamming distance at least four do not exist. The proof relies on existence conditions for the strongly regular graph built on ... 详细信息

关键词： Hamming distance

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REVERSIBLE DATA HIDING IN ENCRYPTED IMAGE BASED ON BLOCK HISTOGRAM SHIFTING

REVERSIBLE DATA HIDING IN ENCRYPTED IMAGE BASED ON BLOCK HIS...

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IEEE International Conference on Acoustics, Speech and signal processing

作者： Zhaoxia Yin Andrew Abel Xinpeng Zhang Bin Luo School of Communication and Information Engineering Shanghai University Shanghai 200072 P.R. China. Computing Science and Mathematics University of Stirling. Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei 230601 P.R. China.

ISBN: (纸本)9781479999897

Since there is good potential for practical applications such as encrypted image authentication, content owner identification and privacy protection, reversible data hiding in encrypted image (RDHEI) has attracted increasing attention in recent years. In this paper, we propose and evaluate a new separable RDHEI framework. Additional data can be embedded into a cipher image previously encrypted using Josephus traversal and a stream cipher. A block histogram shifting (BHS) approach using self-hidden peak pixels is adopted to perform reversible data embedding. Depending on the keys held, legal receivers can extract only the embedded data with the data hiding key, or, they can decrypt an image very similar to the original with the decryption key. They can extract both the embedded data and recover the original image error-free if both keys are availab.e. The results demonstrate that higher embedding payload, better quality of decrypted-marked image and error-free image recovery are achieved.

关键词： signal processing in encrypted domain (SPED) Reversible data hiding in encrypted images (RDHEI) Privacy protection Hidden data images keys keys Cays Imagery (Psychotherapy) embedding Image Error-free operations Locks

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Diffusion tensor based curvature-driven difiusion model for digital inpainting

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Journal of Computational Information Systems 2015年第1期11卷 185-194页

作者： Qu, Lei Huang, Linsheng Wei, Sui Key Lab of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

In this paper, we formulate the digital inpainting as a curvature-driven anisotropic concentration difiusing process. The difiusion strength along the gradient directions is defined as a function of curvature, so that the "Connectivity Principle" can be satisfied. Difierent to the classic Curvature-Driven Difiusion (CDD) model, the difiusion along the edge directions can be easily integrated into the difiusion equation in our model by using difiusion tensor. As result, more exibility in designing the local difiusion behavior is allowed, and the CDD model can be generalized as a special case. We show that our model can boost inpainting speed 3fi6 times and at the same time has similar inpainting quality compared to the widely used CDD model. Experimental results on synthetic and real images have verified the stability and eficiency of our model. ©, 2014, Journal of Computational Information Systems. All right reserved.

关键词： Tensors

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Dynamic clustering algorithm in mobile wireless sensor network

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Journal of Information and Computational Science 2015年第13期12卷 4897-4907页

作者： Wang, Bingting Liang, Hui Lin, Qibin Zhang, Yangyi Wang, Yujie Hong, Peilin Hu, Yanjun Department of Electronic and Electrical Engineering Chuzhou University Chuzhou China Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

In this paper, we analyzed the blindness of traditional clustering algorithms, which select cluster head based on residual energy. Then we proposed the Dynamic Clustering Algorithm (DCA) in Mobile Wireless Sensor Network, which takes into consideration residual buffer level when selecting cluster heads and allots time slots dynamically base on the occupied buffer level. We analyzed the energy model of the proposed DCA algorithm and calculated the optimal distance between cluster heads. Simulation results show that our DCA clustering algorithm has good performances in terms of residual energy, data transmission and network lifetime by comparing it with LEACH and LEACH-CI. ©, 2015, Journal of Information and Computational Science. All right reserved.

关键词： Clustering algorithms

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Person re-identification with density-distance unsupervised salience learning 8th

Person re-identification with density-distance unsupervised ...

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8th International Conference on Image and Graphics, ICIG 2015

作者： Zhou, Baoliang Zheng, Aihua Jiang, Bo Li, Chenglong Tang, Jin Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology AnHui University Hefei China Key Laboratory of Industry Image Processing and Analysis in Anhui Province Hefei China

ISBN: (纸本)9783319219684

Human salience of pedestrians images is distinctive and has been shown importantly in person re-identification (or pedestrians identification) problem. Thus, how to obtain the salient area of pedestrian images is important for this salience based pedestrians identification problem. In this paper, we first show that this kind of salient area detection can be formulated as a kind of outlier detection problem, and then propose a novel unsupervised salience learning method using a local outlier-detection technique for person re-identification task. The main feature of the proposed salience computation method is that it exploits both distance and density information simultaneously. Experimental results on several datasets show the effectiveness of the proposed salience based person re-identification method. © Springer International Publishing Switzerland 2015.

关键词： Statistics

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An image compression method based on block truncation coding and linear regression

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Journal of Information Hiding and Multimedia signal processing 2016年第1期7卷 198-215页

作者： Lyu, Wan-Li Chang, Chin-Chen Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230039 China Department of Information Engineering and Computer Science Feng Chia University 100 Wenhwa Rd Seatwen Taichung40724 Taiwan Department of Computer Science and Information Engineering Asia University Taichung41354 Taiwan

This paper describes an image compression method based on block truncation coding (BTC) and linear regression coding (LRC) hybrid strategy. BTC is simple and easy to use, but only two representative values can not adequately represent the visual feature of image blocks. However, LRC can obtain different values of points with the line function by recording the coefficient of the regression line while the data distribution shows a linear relationship. The proposed method designs a hybrid strategy to combine the advantages of the two methods to improve image quality. In the paper, first, models are designed to rearrange the data of image blocks to satisfy a linear distribution, and then these models are used to code the image blocks. The experimental results indicated that the proposed method is superior to the BTC scheme in terms of image quality, and it can be used for image compression applications. © 2016.

关键词： Linear regression

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Complete separable reversible data hiding in encrypted image 1st

Complete separable reversible data hiding in encrypted image

引用

1st International Conference on Cloud computing and Security, ICCCS 2015

作者： Zhaoxia, Yin Huabin, Wang Haifeng, Zhao Bin, Luo Xinpeng, Zhang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China School of Communication and Information Engineering Shanghai University Shanghai200072 China

ISBN: (纸本)9783319270500

Reversible data hiding in encrypted image (RDHEI) is an emerging technology since it has good potential for practical applications such as encrypted image authentication, content owner identification and privacy protection. But there is one key problem of many existing published works, that the embedded data only can be extracted either before or after image decryption. In this paper, a complete separable reversible data hiding scheme in encrypted images is proposed. Additional data can be embedded into a cipher image which is encrypted by RC4 and can be extracted error-free both from the cipher domain and the plaintext domain. Moreover, the proposed method is simpler to calculate, while offering better performance. The results demonstrate that larger payload, better image quality, and error-free data extraction as well as image recovery are achieved. © Springer International Publishing Switzerland 2015.

关键词： Data privacy

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