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arXiv 2019年

作者： Fan, Zhun Wang, Zhaojun Li, Wenji Yuan, Yutong You, Yugen Yang, Zhi Sun, Fuzan Ruan, Jie Li, Zhaocheng Department of Electronic Engineering Shantou University Guangdong China Key Lab of Digital Signal and Image Processing of Guangdong Province Shantou University Guangdong China

In dealing with constrained multi-objective optimization problems (CMOPs), a key issue of multi-objective evolutionary algorithms (MOEAs) is to balance the convergence and diversity of working populations. However, most state-of-the-art MOEAs show poor performance in balancing them, and can easily cause the working populations to concentrate on a part of regions of the Pareto fronts, leading to a serious imbalanced searching between preserving diversity and achieving convergence. This paper proposes a method which combines a multi-objective to multi-objective (M2M) approach with the push and pull search (PPS) framework, namely PPS-M2M. To be more specific, the proposed algorithm decomposes a CMOP into a set of simple CMOPs. Each simple CMOP corresponds to a sub-population and is solved in a collaborative manner. When dealing with constraints, each sub-population follows a procedure of "ignore the constraints in the push stage and consider the constraints in the pull stage", which helps each working sub-population get across infeasible regions. In order to evaluate the performance of the proposed PPS-M2M, it is compared with the other six algorithms, including M2M, MOEA/D-Epsilon, MOEA/D-SR, MOEA/D-CDP, C-MOEA/D and NSGA-II-CDP on a set of benchmark CMOPs. The experimental results show that the PPS-M2M is significantly better than the other six algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Constraint handling

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Wavelet-Energy-Weighted Local Binary Pattern Analysis for Tire Tread Pattern Classification

Wavelet-Energy-Weighted Local Binary Pattern Analysis for Ti...

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International Conference on Imaging, Signal processing and communication (ICISPC)

作者： Ying Liu Shuai Zhang Fuping Wang Kengpang Lim Qiqi Liu Yanbo Lei Yanchao Gong Jin Lu National Key Lab. of Electronic Information Processing for Applications in Crime Scene Investigation Ministry of Public Security China Center for Image and Information Processing Xi'an University of Posts and Telecommunications China

Tire tread pattern image classification plays an important role in crime scene and traffic accident investigation. Due to the lack of standard test dataset, there is little work done in this area. For efficient texture feature description, inherent characteristic of tire patterns need to be considered. Leveraging on the directionality characteristics of tread patterns, a novel texture feature extraction algorithm is proposed based on adaptive weighted feature fusion with the weights defined by sub-band energy ratio. The proposed approach consists of: (1) discrete wavelet decomposition of tire tread image to obtain low frequency, horizontal, vertical and diagonal sub-bands; (2) extraction of rotation-invariant uniform local binary pattern features from the sub-band images; (3) concatenating the tread pattern directional features, weighted by their corresponding sub-band energies. Applying SVM for tire tread pattern classification, experimental results on real-world tire tread patterns show that the proposed texture feature extraction algorithm is outperforms other prior methods.

关键词： Tires Feature extraction Discrete wavelet transforms Information processing Telecommunications image reconstruction Support vector machines

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An adaptive multi-scale tracking method based on Kernelized Correlation Filter 14th

An adaptive multi-scale tracking method based on Kernelized ...

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14th International Forum of Digital TV and Wireless Multimedia communication, IFTC 2017

作者： Xu, Qiling Yang, Hua Institue of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai China

ISBN: (纸本)9789811081071

Visual tracking is a fundamental computer vision task with a wide range of applications. Kernelized Correlation Filter (KCF) is an excellent algorithm with high tracking speed. However, the target tracking scale in the KCF algorithm is a fixed value which might cause tracking failure or target drifting problem when the target scale changes significantly. In this paper, we present an adaptive multi-scale tracking algorithm based on the KCF algorithm by estimating the scale of the target. Our method builds upon the correlation filter with a Gaussian kernel and reasonable prediction of the target size. In order to verify the effectiveness of the proposed algorithm, 9 sets of complex video sequences of a commonly used tracking benchmark were selected and the results were compared with other tracking methods (KCF, CSK, CT, TLD, Struck, CNN-SVM and MDNet). The results show that the proposed method has high accuracy. The method in this paper has strong robustness in the complex scenes with challenges of scale variation, illumination variation, occlusion, in-plane rotation, out-of plane rotation and deformation. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Target tracking

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Observer-Based Robust Containment Control of Multi-agent Systems With Input Saturation

Observer-Based Robust Containment Control of Multi-agent Sys...

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Chinese Control Conference (CCC)

作者： Juan Qian Xiaoling Wang Guo-Ping Jiang Housheng Su College of Automation and College of Artificial Intelligence Nanjing University of Posts and Telecommunications and Jiangsu Engineering Lab for IOT Intelligent Robots(IOTRobot) Nanjing PR China School of Artificial Intelligence and Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry ofChina Huazhong University of Science and Technology Wuhan PR China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

In this paper, the robust containment control problem of the leader-following multi-agent systems with input saturation and input additive disturbance is addressed, where the followers can be informed by multiple leaders. With the help of the lowand-high gain feedback technique and the high-gain observer approach, a distributed control algorithm for each agent is firstly designed by using the observed output information, then sufficient conditions are provided to guarantee the semi-global robust containment of the system. Finally, some numerical simulations are given to verify the correctness of the theoretical results.

关键词： Multi-agent systems Robustness Observers Electronic mail State feedback Additives

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Ensemble of deep convolutional neural networks for automatic pavement crack detection and measurement

arXiv

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arXiv 2020年

作者： Fan, Zhun Li, Chong Chen, Ying Di Mascio, Paola Chen, Xiaopeng Zhu, Guijie Loprencipe, Giuseppe Key Lab of Digital Signal and Image Processing of Guangdong Province Shan’tou515063 China College of Engineering Shantou University Shan’tou515063 China Department of Civil Construction and Environmental Engineering Sapienza University of Rome Rome00184 Italy Department of Industrial Engineering Pusan National University Busan609735 Korea Republic of

Automated pavement crack detection and measurement are important road issues. Agencies have to guarantee the improvement of road safety. Conventional crack detection and measurement algorithms can be extremely time-consuming and low efficiency. Therefore, recently, innovative algorithms have received increased attention from researchers. In this paper, we propose an ensemble of convolutional neural networks (without a pooling layer) based on probability fusion for automated pavement crack detection and measurement. Specifically, an ensemble of convolutional neural networks was employed to identify the structure of small cracks with raw images. Secondly, outputs of the individual convolutional neural network model for the ensemble were averaged to produce the final crack probability value of each pixel, which can obtain a predicted probability map. Finally, the predicted morphological features of the cracks were measured by using the skeleton extraction algorithm. To validate the proposed method, some experiments were performed on two public crack databases (CFD and AigleRN) and the results of the different state-of-the-art methods were compared. To evaluate the efficiency of crack detection methods, three parameters were considered: precision (Pr), recall (Re) and F1 score (F1). For the two public databases of pavement images, the proposed method obtained the highest values of the three evaluation parameters: for the CFD database, Pr = 0.9552, Re = 0.9521 and F1 = 0.9533 (which reach values up to 0.5175 higher than the values obtained on the same database with the other methods), for the AigleRN database, Pr = 0.9302, Re = 0.9166 and F1 = 0.9238 (which reach values up to 0.7313 higher than the values obtained on the same database with the other methods). The experimental results show that the proposed method outperforms the other methods. For crack measurement, the crack length and width can be measure based on different crack types (complex, common, thin, and inter

关键词： Convolution

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Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review

arXiv

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arXiv 2021年

作者： Wu, Dongrui Xu, Jiaxin Fang, Weili Zhang, Yi Yang, Liuqing Xu, Xiaodong Luo, Hanbin Yu, Xiang The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China The School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China The College of Public Administration Huazhong University of Science and Technology Wuhan430074 China The University of Michigan Ann ArborMI48109 United States The School of Management and Sino European Institute for Intellectual Property Huazhong University of Science and Technology Wuhan430074 China

Physiological computing uses human physiological data as system inputs in real time. It includes, or significantly overlaps with, brain-computer interfaces, affective computing, adaptive automation, health informatics, and physiological signal based biometrics. Physiological computing increases the communication bandwidth from the user to the computer, but is also subject to various types of adversarial attacks, in which the attacker deliberately manipulates the training and/or test examples to hijack the machine learning algorithm output, leading to possible user confusion, frustration, injury, or even death. However, the vulnerability of physiological computing systems has not been paid enough attention to, and there does not exist a comprehensive review on adversarial attacks to them. This paper fills this gap, by providing a systematic review on the main research areas of physiological computing, different types of adversarial attacks and their applications to physiological computing, and the corresponding defense strategies. We hope this review will attract more research interests on the vulnerability of physiological computing systems, and more importantly, defense strategies to make them more secure. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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BoostTree and BoostForest for Ensemble Learning

arXiv

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arXiv 2020年

作者： Zhao, Changming Wu, Dongrui Huang, Jian Yuan, Ye Zhang, Hai-Tao Peng, Ruimin Shi, Zhenhua Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China The Autonomous Intelligence Unmanned Systems Engineering Research Center of Ministry of Education of China The State Key Lab of Digital Manufacturing Equipment and Technology Wuhan China

Bootstrap aggregating (Bagging) and boosting are two popular ensemble learning approaches, which combine multiple base learners to generate a composite model for more accurate and more reliable performance. They have been widely used in biology, engineering, healthcare, etc. This paper proposes BoostForest, which is an ensemble learning approach using BoostTree as base learners and can be used for both classification and regression. BoostTree constructs a tree model by gradient boosting. It increases the randomness (diversity) by drawing the cut-points randomly at node splitting. BoostForest further increases the randomness by bootstrapping the training data in constructing different BoostTrees. BoostForest generally outperformed four classical ensemble learning approaches (Random Forest, Extra-Trees, XGBoost and LightGBM) on 35 classification and regression datasets. Remarkably, BoostForest tunes its parameters by simply sampling them randomly from a parameter pool, which can be easily specified, and its ensemble learning framework can also be used to combine many other base learners. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

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A novel bimodal emotion database from physiological signals and facial expression

A novel bimodal emotion database from physiological signals ...

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作者： Yan, Jingjie Wang, Bei Liang, Ruiyu Jiangsu Provincial Key Laboratory of Image Processing and Image Communication College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China Research Center for Learning Science Southeast University Nanjing210096 China School of Communication Engineering Nanjing Institute of Technology Nanjing211167 China

In this paper, we establish a novel bimodal emotion database from physiological signals and facial expression, which is named as PSFE. The physiological signals and facial expression of the PSFE database are respectively recorded by the equipment of the BIOPAC MP 150 and the Kinect for Windows in the meantime. The PSFE database altogether records 32 subjects which include 11 women and 21 man, and their age distribution is from 20 to 25. Moreover, the PSFE database records three basic emotion classes containing calmness, happiness and sadness, which respectively correspond to the neutral, positive and negative emotion state. The general sample number of the PSFE database is 288 and each emotion class contains 96 samples. © Copyright 2018 The Institute of Electronics Information and communication Engineers.

关键词： Database systems

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Learning Based Estimation of Video Coding Distortion

Learning Based Estimation of Video Coding Distortion

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Xiangwen Wang Zhengyi Luo Pengbo Li Li Song College of Electronics and Information Engineering Shanghai University of Electric Power Shanghai China Shanghai University of Electric Power Shanghai China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University 2 MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

Coding distortion is a critical factor in video coding algorithms such as rate distortion optimization, rate control and optimal quantization. Accurate distortion estimation without complex pre-coding has always been highly desired. Traditional distortion estimation methods depend roughly on two assumptions—accurate modeling of the DCT coefficients and fine quantization. Unfortunately, these two assumptions may not be satisfied in most video coding situations. This paper proposes two new methods to estimate video coding distortion dropping the above assumptions. The first one is based on function fitting, modeling coding distortion as a function of the ratio of quantization step to the standard deviation of the residual image. The second one is machine learning based, where an ensemble learning model is employed and the feature vector is composed of the distortion of referenced frames, the variance of the residual image and the quantization step. The HEVC reference encoder HM with the hierarchical coding structure is employed for accuracy evaluation. The experimental results show the accuracy of the proposed methods is much higher than that of traditional ones.

关键词： Distortion Quantization (signal) Estimation Discrete cosine transforms Encoding Laplace equations Video coding

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Deep Video Inverse Tone Mapping

Deep Video Inverse Tone Mapping

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IEEE International Conference on Multimedia Big Data (BigMM)

作者： Yucheng Xu Li Song Rong Xie Wenjun Zhang Institute of Image Communication and Network Engineering Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University

Inverse tone mapping is an important topic in High Dynamic Range technology. Recent years, deep learning based image inverse tone mapping methods have been extensively studied and perform better than classical inverse tone mapping methods. However, these methods consider the inverse tone mapping problem as a domain transformation problem from LDR domain directly to HDR domain and ignore the relationship between LDR and HDR. Besides, when using these deep learning based methods to transform frames of videos, it will lead to temporal inconsistency and flickering. In this work, we propose a new way to consider the inverse tone mapping problem and design a deep learning based video inverse tone mapping algorithm to reduce the flickering. Different from previous methods, we first transform LDR resources back to approximate real scenes and use these real scenes to generate the HDR outputs. When generating HDR outputs, we use 3D convolutional neural network to reduce the flickering. We also use methods to further constrain the luminance information and the color information of HDR outputs separately. Finally, we compare our results with existing classical video inverse tone mapping algorithms and deep image inverse tone mapping methods to show our great performance, and we also prove the necessity of each part of our method.

关键词： Machine learning Three-dimensional displays Transforms Video sequences Decoding image color analysis Convolution

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