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Journal of Real-Time Image processing

作者： Tang, Zhenjun Xu, Shijie Ye, Dengpan Wang, Jinyan Zhang, Xianquan Yu, Chuanqiang Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin541004 China Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing Guangxi Normal University Guilin541004 China School of Cyber Science and Engineering Wuhan University Wuhan430072 China School of Computer Science and Information Technology Guangxi Normal University No. 15 YuCai Road Guilin541004 China

Reversible data hiding in encrypted image (RDH-EI) is a hot topic of data hiding in recent years. Most RDH-EI algorithms do not reach desirable embedding rate and their computational costs are not suitable for real-time applications. Aiming at these problems, we propose an efficient RDH-EI algorithm with shifting block histogram of pixel differences in homomorphic encrypted domain. A key step of our RDH-EI algorithm is the block-based encryption scheme with additive homomorphism, which can preserve spatial correlation of plaintext image in homomorphic encrypted domain. In addition, our proposed technique of shifting block histogram can achieve efficient data embedding with high payload and correctly recover image. Extensive experiments are conducted to validate performance of our RDH-EI algorithm. Comparison results illustrate that our RDH-EI algorithm outperforms some state-of-the-art algorithms in terms of embedding rate, visual quality and computational time. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Cryptography Graphic methods Image processing Pixels Steganography Computational costs Computational time Histogram shifting Homomorphic cryptosystem Real time application Reversible data hiding Spatial correlations State of the art algorithms

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Plant Leaf Recognition Based on Conditional Generative Adversarial Nets 15th

Plant Leaf Recognition Based on Conditional Generative Adver...

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15th International Conference on intelligent Computing, ICIC 2019

作者： Jiao, Zhihao Zhang, Lijun Yuan, Chang-An Qin, Xiao Shang, Li Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Collaborative Innovation Center of Intelligent New Energy Vehicle Shanghai China School of Automotive Studies Tongji University Shanghai China Science Computing and Intelligent Information Processing of GuangXi Higher Education Key Laboratory Nanning Normal University NanningGuangxi China Department of Communication Technology College of Electronic Information Engineering Suzhou Vocational University SuzhouJiangsu215104 China

ISBN: (纸本)9783030267629

Plants play an important role in human life, Identifying and protecting plants has far-reaching implications for the sustainable development of the ecological environment. Plant leaves can often reflect important characteristics of plants, so it is scientific and feasible to effectively identify plant species through plant leaves. With the rapid development of deep learning in recent years, it has been widely applied in plant leaf recognition. Compared with the traditional method, deep learning based plant leaf recognition algorithm can extract plant leaf features more effectively and can greatly improve the performance. Based on the detailed analysis of the structural characteristics of three classical convolutional neural network models, this paper comprehensively compares the recognition performance of three convolutional neural network models on ICL plant leaf datasets. The experimental results show that the Conditional Generative Adversarial Net with optimized output layer has better recognition results on the plant leaf dataset than other convolutional neural network models. © 2019, Springer Nature Switzerland AG.

关键词： Neural network models

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A hybrid NSGA-II for matching biomedical ontology 14th

A hybrid NSGA-II for matching biomedical ontology

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14th International Conference on intelligent information Hiding and Multimedia Signal processing, IIH-MSP 2018

作者： Xue, Xingsi Chen, Jie Chen, Junfeng Chen, Dongxu College of Information Science and Engineering Fujian University of Technology FuzhouFujian350118 China Intelligent Information Processing Research Center Fujian University of Technology FuzhouFujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology FuzhouFujian350118 China Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology FuzhouFujian350118 China College of IOT Engineering Hohai University ChangzhouJiangsu China Fujian Medical University Union Hospital FuzhouFujian350001 China

ISBN: (纸本)9783030037444

Over the recent years, ontologies are widely used in the biomedical domains. However, biomedical ontology heterogeneity problem hamper the cooperation between intelligent applications based on biomedical ontologies. It is crucial to establish correspondences between the heterogeneous biomedical concepts in different ontologies, which is so-called biomedical ontology matching. Approaches based on Multi-Objective Evolutionary Algorithm (MOEA), such as NSGA-II, are emerging as a new methodology to solve the ontology matching problem. In this paper, to further improve the quality of biomedical ontology alignments, a hybrid NSGA-II is proposed, which modifies the knee solutions in the Pareto front by using a local search method. Experiment utilizes two biomedical ontology matching tracks provided by Ontology Alignment Evaluation Initiative (OAEI 2017). The experimental results show that our approach outperforms the participants of OAEI 2017 and NSGA-II based ontology matching technique. © Springer Nature Switzerland AG 2019.

关键词： Ontology

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New Framework for Code-Mapping-based Reversible Data Hiding in JPEG Images

arXiv

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arXiv 2020年

作者： Du, Yang Yin, Zhaoxia Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China Anhui Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China School of Communication & Electronic Engineering East China Normal University Shanghai200241 China

Code mapping (CM) is an efficient technique for reversible data hiding (RDH) in JPEG images, which embeds data by constructing a mapping relationship between the used and unused codes in the JPEG bitstream. This study presents a new framework for designing a CM-based RDH method. First, a new code mapping strategy is proposed to suppress file size expansion and improve applicability. Based on our proposed strategy, the mapped codes are redefined by creating a new Huffman table rather than selecting them from the unused codes in the original Huffman table. The critical issue of designing the CM-based RDH method, that is, constructing code mapping, is converted into a combinatorial optimization problem. This study proposes a novel CM-based RDH method that utilizes a genetic algorithm (GA). The experimental results demonstrate that the proposed method achieves a high embedding capacity with no signal distortion while suppressing file size expansion. Copyright © 2020, The Authors. All rights reserved.

关键词： Mapping

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Separable reversible data hiding based on MSB prediction for encrypted 3D mesh models?

arXiv

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arXiv 2019年

作者： Xu, Na Yin, Zhaoxia Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei

Reversible data hiding in encrypted domain (RDH-ED) has received tremendous attention from the research community because data can be embedded into cover media without exposing it to the third party data hider and the cover media can be losslessly recovered after the extraction of the embedded data. Although, in recent years, exten- sive studies have been carried out about images based RDH-ED, little attention is paid to RDH-ED in 3D mesh models due to its complex data structure and irregular geometry. We propose a separable RDH-ED method for 3D mesh models based on MSB (most significant bit) predic- tion. Firstly, we divide all the vertices of the mesh into the "embedded" set and "reference" set according to topological information between ver- tices, and calculate the MSB prediction errors for the vertices of the "em- bedded" set. Then, bit-stream encryption is used to encrypt the mesh. Finally, data is embedded by replacing the MSB of the encrypted vertex coordinates of the "embedded" set by the additional data bits. Recipient with data hiding key can correctly extract the additional data from the marked encrypted mesh. Recipient with encryption key can recover the mesh perfectly by using MSB prediction. Experimental results show that the proposed method has higher embedding capacity and higher quality of the recovered meshes compared to the existing RDH-ED method for 3D meshes based on symmetric encryption. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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Deep Metric Learning via Adaptive Learnable Assessment

Deep Metric Learning via Adaptive Learnable Assessment

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wenzhao Zheng Jiwen Lu Jie Zhou Department of Automation Tsinghua University China State Key Lab of Intelligent Technologies and Systems China Beijing National Research Center for Information Science and Technology China Tsinghua Shenzhen International Graduate School Tsinghua University China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

In this paper, we propose a deep metric learning via adaptive learnable assessment (DML-ALA) method for image retrieval and clustering, which aims to learn a sample assessment strategy to maximize the generalization of the trained metric. Unlike existing deep metric learning methods that usually utilize a fixed sampling strategy like hard negative mining, we propose a sequence-aware learnable assessor which re-weights each training example to train the metric towards good generalization. We formulate the learning of this assessor as a meta-learning problem, where we employ an episode-based training scheme and update the assessor at each iteration to adapt to the current model status. We construct each episode by sampling two subsets of disjoint labels to simulate the procedure of training and testing and use the performance of one-gradient-updated metric on the validation subset as the meta-objective of the assessor. Experimental results on the widely used CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate the effectiveness of the proposed approach.

关键词： Measurement Training Adaptation models Task analysis Learning systems Complexity theory Market research

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Structure-preserving super resolution with gradient guidance

arXiv

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arXiv 2020年

作者： Ma, Cheng Rao, Yongming Cheng, Yean Chen, Ce Lu, Jiwen Zhou, Jie Department of Automation Tsinghua University China State Key Lab of Intelligent Technologies and Systems China Beijing National Research Center for Information Science and Technology China Tsinghua Shenzhen International Graduate School Tsinghua University China

Structures matter in single image super resolution (SISR). Recent studies benefiting from generative adversarial network (GAN) have promoted the development of SISR by recovering photo-realistic images. However, there are always undesired structural distortions in the recovered images. In this paper, we propose a structure-preserving super resolution method to alleviate the above issue while maintaining the merits of GAN-based methods to generate perceptual-pleasant details. Specifically, we exploit gradient maps of images to guide the recovery in two aspects. On the one hand, we restore high-resolution gradient maps by a gradient branch to provide additional structure priors for the SR process. On the other hand, we propose a gradient loss which imposes a second-order restriction on the super-resolved images. Along with the previous image-space loss functions, the gradient-space objectives help generative networks concentrate more on geometric structures. Moreover, our method is model-agnostic, which can be potentially used for off-the-shelf SR networks. Experimental results show that we achieve the best PI and LPIPS performance and meanwhile comparable PSNR and SSIM compared with state-of-the-art perceptual-driven SR methods. Visual results demonstrate our superiority in restoring structures while generating natural SR images. 1 Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Polyvinyl chloride reinforced soft silicone curvature sensor with optical fiber implantation

引用

Optik 2019年 177卷 26-35页

作者： Guangkai Sun Xu Zhang Mingli Dong Yanlin He Mingxin Yu Lianqing Zhu Beijing Key Laboratory of Optoelectronic Measurement Technology Beijing Information Science & Technology University Beijing 100101 China Bionic and Intelligent Equipment Lab Beijing Information Science and Technology University Beijing 100016 China

Soft curvature sensors have huge demand in the emerging field of soft robots, rehabilitative robots and morphing flight vehicles. However, the current soft sensors have limitations in sensitivity, repeatability, and fabrication cost, which hinder their practical applications. To address these issues, we propose a soft curvature sensor with soft silicone-polyvinyl chloride mixed substrate and fiber Bragg gratings (FBGs). The major contribution is obtaining a soft curvature sensor with extraordinary high sensitivity and repeatability. Using the pure bending model, the nonlinear relationship between the bending curvature and Bragg wavelength shift is analyzed. The reflection spectrums of the FBGs in the mixed substrate are measured, and the effect of the horizontal position and embedded depth of the FBG is analyzed experimentally. The optimal embedded position of the FBG and the sensitivity and repeatability of the soft curvature sensor is obtained. The results proved that the proposed sensor is soft enough to follow the contour of the object for real time measurement, the maximum sensitivity is up to 328.67pm/m −1 in the measurement range of 0m −1 ∼15m −1 , and the fluctuation coefficient of repeated measurement is less than 0.07. The sensor is simple in structure, easy to fabricate and low cost. All these make it to be a promising approach for practical applications in the industrial and medical field.

关键词： Soft sensor Curvature sensor Optical fiber Soft robotics

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Structure-Preserving Super Resolution With Gradient Guidance

Structure-Preserving Super Resolution With Gradient Guidance

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Cheng Ma Yongming Rao Yean Cheng Ce Chen Jiwen Lu Jie Zhou Department of Automation Tsinghua University China State Key Lab of Intelligent Technologies and Systems China Beijing National Research Center for Information Science and Technology China Tsinghua Shenzhen International Graduate School Tsinghua University China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

关键词： Image resolution Image edge detection Distortion Image reconstruction Image restoration Gallium nitride Visualization

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Neighborhood Interval Observer Based Coordination Control for Multi-agent Systems with Disturbances ⁎

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IFAC-PapersOnLine 2020年第2期53卷 10994-10999页

作者： Xiaoling Wang Guo-Ping Jiang Wen Yang Housheng Su Xiaofan Wang College of Automation Nanjing University of Posts and Telecommunications Nanjing 210023 China and also with Jiangsu Engineering Lab for IOT Intelligent Robots (IOTRobot) Nanjing 210023 China Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai 200237 China School of Artifcial Intelligence and Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Luoyu Road 1037 Wuhan 430074 China Department of Automation Shanghai University Shanghai 200072 China

This paper focuses on multi-agent systems with uncertain disturbances, in which only the bounding functions on the disturbances and the bounds on the initial state of each agent are known. By designing a neighborhood interval observer for this kind of multi-agent system, the estimation of the sum of the relative state of each agent associated with itself and its neighbors is frstly realized. Then, on the basis of these estimated information, local control algorithm is designed to drive the system to achieve bounded consensus.

关键词： Multi-agent system disturbance neighborhood interval observer bounded consensus

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