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An automatic detection of green tide using multi-windows with their adaptive threshold from Landsat TM/ETM plus image

引用

Acta Oceanologica Sinica 2017年第11期36卷 106-114页

作者： WANG Changying CHU Jialan TAN Meng SHAO Fengjing SUI Yi LI Shujing School of Data Science and Software Engineering Qingdao UniversityQingdao 266071China Institute of Big Data Technology and Smart City of Qingdao Qingdao 266071China Key laboratory of Marine Red Tide Disaster Three-dimensional Monitoring Technology and Application East China Sea BranchState Oceanic AdministrationShanghai 200080China National Marine Environmental Monitoring Center State Oceanic AdministrationDalian 116023China North China Sea Data and Information Service Center North China Sea BranchState Oceanic AdministrationQingdao 266061China

Since the atmospheric correction is a necessary preprocessing step of remote sensing image before detecting green tide, the introduced error directly affects the detection precision. Therefore, the detection method of green tide is presented from Landsat TM/ETM plus image which needs not the atmospheric correction. In order to achieve an automatic detection of green tide, a linear relationship（y =0.723 x＋0.504） between detection threshold y and subtraction x（x=λnir–λred） is found from the comparing Landsat TM/ETM plus image with the field *** this relationship, green tide patches can be detected automatically from Landsat TM/ETM plus *** there is brightness difference between different regions in an image, the image will be divided into a plurality of windows（sub-images） with a same size firstly, and then each window will be detected using an adaptive detection threshold determined according to the discovered linear relationship. It is found that big errors will appear in some windows, such as those covered by clouds seriously. To solve this problem, the moving step k of windows is proposed to be less than the window width n. Using this mechanism, most pixels will be detected[n/k]×[n/k] times except the boundary pixels, then every pixel will be assigned the final class（green tide or sea water） according to majority rule voting strategy. It can be seen from the experiments, the proposed detection method using multi-windows and their adaptive thresholds can detect green tide from Landsat TM/ETM plus image automatically. Meanwhile, it avoids the reliance on the accurate atmospheric correction.

关键词： automatic detection green tide adaptive threshold Landsat TM/ETM plus image

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Deep Text Classification Can be Fooled

arXiv

引用

arXiv 2017年

作者： Liang, Bin Li, Hongcheng Su, Miaoqiang Bian, Pan Li, Xirong Shi, Wenchang School of Information Renmin University of China Beijing China Key laboratory of Data Engineering and Knowledge Engineering MOE Beijing China

In this paper, we present an effective method to craft text adversarial samples, revealing one important yet underestimated fact that DNN-based text classifiers are also prone to adversarial sample attack. Specifically, confronted with different adversarial scenarios, the text items that are important for classification are identified by computing the cost gradients of the input (white-box attack) or generating a series of occluded test samples (black-box attack). Based on these items, we design three perturbation strategies, namely insertion, modification, and removal, to generate adversarial samples. The experiment results show that the adversarial samples generated by our method can successfully fool both state-of-the-art character-level and word-level DNN-based text classifiers. The adversarial samples can be perturbed to any desirable classes without compromising their utilities. At the same time, the introduced perturbation is difficult to be perceived. Copyright © 2017, The Authors. All rights reserved.

关键词： Classification (of information)

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Research on Target Extraction Technology of Fruit and Vegetable Images in the Complex Environment

Research on Target Extraction Technology of Fruit and Vegeta...

引用

The Third International Conference of Pioneering Computer Scientists, Engineers and Educators,ICPCSEE 2017(originally ICYCSEE)

作者： Yanqing Wang Hao Zheng Key Laboratory of Trusted Cloud Computing and Big Data Analysis School of Information Engineering Nanjing Xiaozhuang University

Considering the difficulty of fruit and vegetable images with uneven illumination and uncontrolled backgrounds, this paper proposed an image preprocess algorithm based on visual subject detection. Firstly, we can use manifold ranking to significance test and to acquire significance images, then use gradient images and position weighting to get cutting images, finally adjust the image brightness and remove image noise to complete the image *** experiment results demonstrated the robustness and real-time of the proposed algorithm which can segment images accurately and the accuracy is higher than 91%.

关键词： Manifold ranking Significance images Gradient images Image segment

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Information Integration and Environmental Monitoring for Cage Pigeons

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IOP Conference Series: Earth and Environmental Science 2019年第3期371卷

作者： Xin Chai Zhifen Zhai Jianhua Zhang Fantao Kong Jianzhai Wu Wei Sun Xiangyang Zhou Shuqing Han Agricultural Information Institute Chinese Academy of Agricultural Sciences/Key Laboratory of Agricultural Big Data Ministry of Agriculture and Rural Affairs Beijing 100081 P. R. China Chinese Academy of Agricultural Engineering Beijing 100125 P. R. China

Environmental changes in the farm can have a significant impact on the healthy growth of cage pigeons, disease prevention and the quality of pigeon meat. The stability and adaptability testing of environmental monitoring equipment and systems was completed through the pilot deployment of IoT Ranch in a pigeon farm in Beijing. An integrated program for real-time monitoring of environmental parameters of cage pigeons is implemented. Moreover, a system for informatizing and researching environmental information and real-time querying information on a webpage or mobile terminal is developed. The information-integrated environmental monitoring equipment and system can provide technical support for the modernization, automation and digital management of cage pigeons.

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Crossbar-Net: A novel convolutional network for kidney tumor segmentation in CT images

arXiv

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arXiv 2018年

作者： Yu, Qian Shi, Yinghuan Sun, Jinquan Gao, Yang Zhu, Jianbing Dai, Yakang State Key Laboratory for Novel Software Technology National Research Institute for Big Data Science in Health and Medicine Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing University China School of Data and Computer Science Shandong Women’s University China Suzhou Science and Technology Town Hospital China Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences China

Due to the unpredictable location, fuzzy texture and diverse shape, accurate segmentation of the kidney tumor in CT images is an important yet challenging task. To this end, we in this paper present a cascaded trainable segmentation model termed as Crossbar-Net. Our method combines two novel schemes: (1) we originally proposed the crossbar patches, which consists of two orthogonal non-squared patches (i.e., the vertical patch and horizontal patch). The crossbar patches are able to capture both the global and local appearance information of the kidney tumors from both the vertical and horizontal directions simultaneously. (2) With the obtained crossbar patches, we iteratively train two sub-models (i.e., horizontal sub-model and vertical sub-model) in a cascaded training manner. During the training, the trained sub-models are encouraged to become more focus on the difficult parts of the tumor automatically (i.e., mis-segmented regions). Specifically, the vertical (horizontal) sub-model is required to help segment the mis-segmented regions for the horizontal (vertical) sub-model. Thus, the two sub-models could complement each other to achieve the self-improvement until convergence. In the experiment, we evaluate our method on a real CT kidney tumor dataset which is collected from 94 different patients including 3,500 CT slices. Compared with the state-of-the-art segmentation methods, the results demonstrate the superior performance of our method on the Dice similarity coefficient, true positive fraction, centroid distance and Hausdorff distance. Moreover, to exploit the generalization to other segmentation tasks, we also extend our Crossbar-Net to two related segmentation tasks: (1) cardiac segmentation in MR images and (2) breast mass segmentation in X-ray images, showing the promising results for these two tasks. Our implementation is released at https: //***/Qianyu1226/*** Codes 68T45, 68T05 Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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A laying hen breeding environment monitoring system based on internet of things

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IOP Conference Series: Earth and Environmental Science 2019年第3期371卷

作者： Zhifen Zhai Jianhua Zhang Xin Chai Fantao Kong Jianzhai Wu Jing Zhang Shuqing Han Chinese Academy of Agricultural Engineering Beijing 100125 P. R. China Agricultural Information Institute Chinese Academy of Agricultural Sciences/Key Laboratory of Agricultural Big Data Ministry of Agriculture and Rural Affairs Beijing 100081 P. R. China

The breeding environment of laying hens has an important influence on the growth of laying hens, the prevention and control of diseases, and the yield and quality of eggs. Therefore, the real-time collection and utilization of the environment information of laying hens is the key point of breeding laying hens. In this study, the environmental information monitoring and control equipment and systems for egg-breeding laying hens were integrated and developed. The real-time monitoring of the environment information of the laying hens and the information query function on the webpage and mobile devices was realized. The stability and versatility testing of equipment and systems were completed through the pilot deployment of equipment and systems in a laying hen farm in Beijing. The design and development of the equipment and system can provide technical support for the modernization, mechanization and information management of laying hens.

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Low-resolution face recognition in the wild via selective knowledge distillation

arXiv

引用

arXiv 2018年

作者： Ge, Shiming Zhao, Shengwei Li, Chenyu Li, Jia Institute of Information Engineering Chinese Academy of Sciences Beijing100095 China Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security at University of Chinese Academy of Sciences China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

Typically, the deployment of face recognition models in the wild needs to identify low-resolution faces with extremely low computational cost. To address this problem, a feasible solution is compressing a complex face model to achieve higher speed and lower memory at the cost of minimal performance drop. Inspired by that, this paper proposes a learning approach to recognize low-resolution faces via selective knowledge distillation. In this approach, a two-stream convolutional neural network (CNN) is first initialized to recognize high-resolution faces and resolution-degraded faces with a teacher stream and a student stream, respectively. The teacher stream is represented by a complex CNN for high-accuracy recognition, and the student stream is represented by a much simpler CNN for low-complexity recognition. To avoid significant performance drop at the student stream, we then selectively distil the most informative facial features from the teacher stream by solving a sparse graph optimization problem, which are then used to regularize the fine-tuning process of the student stream. In this way, the student stream is actually trained by simultaneously handling two tasks with limited computational resources: approximating the most informative facial cues via feature regression, and recovering the missing facial cues via low-resolution face classification. Experimental results show that the student stream performs impressively in recognizing low-resolution faces and costs only 0.15MB memory and runs at 418 faces per second on CPU and 9, 433 faces per second on GPU. Copyright © 2018, The Authors. All rights reserved.

关键词： Students

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Research on a method of fruit tree pruning based on BP neural network

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Journal of Physics: Conference Series 2019年第4期1237卷

作者： Shiyang Liu Jiaojiao Yao Hui Li Changpeng Qiu Ruijun Liu College of Information and Electrical Engineering China Agricultural University Beijing 100083 China College of Horticulture China Agricultural University Beijing 100083 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University. Beijing 100048 China

The mainstream pruning robots do not have the ability to make decisions independently. The pruning schemes are all artificially generated by experts according to the collected images. In order to improve the intelligence of pruning robot and reduce the labor cost of pruning work, it is necessary to study the robot pruning decision algorithm corresponding to different fruit tree varieties. In this paper, taking apples in the early fruit period as an example, referring to the technical principle of traditional fruit tree pruning and aiming at two types of interference in the pruning process, the back branches and interfering branches, a pruning decision algorithm based on BP neural network was proposed. The algorithm formed the training set by artificially collecting the accurate data of the spatial characteristics of the fruit tree branches and performed calibration for pruning type, and the neural network model was trained according to the calibrated data set. The model trained in the first stage showed the situation that the competition branches cannot be identified. Based on this, an improved algorithm was proposed to improve the classification performance of the competition branches. The experimental results verified that the F1 score of the method for the back branches was 0913; the F1 score for the centripetal branches was 0.867; the improved algorithm has an F1score of 0.755 for the competition branches; the overall conformed to the expectation, which could provide algorithm support for the pruning robot to make artificial intelligence decision.

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Research on 3D skeletal model extraction algorithm of branch based on SR4000

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Journal of Physics: Conference Series 2019年第2期1237卷

A branch 3D Skeleton extraction method based on SR4000 is proposed, which can be used for pruning robot visual recognition. In this method a 3D Skeleton model of the branches of apple trees in the initial fruit stage was successfully constructed. According to the experimental results, the average computational efficiency of the 2AHC(2-level Aggregation Hierarchy Clustering) is increased by 369%, the detection rate is 84.69%, and the error rate is 5.03%. The average detection rate of depth analytic hierarchy process was 64%; The overall 3D Skeleton restoration effect is better. Therefore, the algorithm can better reflect the qualitative relationship between branches, improve the computational efficiency, and provide algorithm support for automatic pruning robot visual recognition.

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A Vehicular Positioning Enhancement with Connected Vehicle Assistance Using Extended Kalman Filtering 1st

A Vehicular Positioning Enhancement with Connected Vehicle A...

引用

1st International Conference on 5G for Future Wireless Networks, 5GWN 2017

作者： Tian, Daxin Liu, Wenhao Duan, Xuting Rong, Hui Guo, Peng Wang, Wenyang Zhang, Haijun Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University XueYuan Road No. 37 Beijing100191 China China Automotive Technology and Research Center Automotive Engineering Research Institute East Xianfeng Road No. 68 Tianjin300300 China School of Computer and Communication Engineering University of Science and Technology Beijing XueYuan Road No. 30 Beijing100083 China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University XueYuan Road No. 37 Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou. 2 Nanjing210096 China Key Lab of Urban ITS Technology Optimization and Integration The Ministry of Public Security of China Hefei230088 China

ISBN: (纸本)9783319728223

In this paper, we consider the problem of vehicular positioning enhancement with emerging connected vehicles (CV) technologies. In order to actually describe the scenario, the Interacting Multiple Model (IMM) filter is used for depicting varies of observation models. A CV-enhanced IMM filtering approach is proposed to locate a vehicle by data fusion from both coarse GPS data and the Doppler frequency shifts (DFS) measured from dedicated short-range communications (DSRC) radio signals. Simulation results state the effectiveness of the proposed approach called CV-IMM-EKF. © 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Global positioning system

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