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Self-propagating video segmentation using patch matching and enhanced Onecut

SN APPLIED SCIENCES 2020年第2期2卷 1-12页

作者： Han, Shoudong Xia, Chenfei Liu, Yujun Yang, Yingchun Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Shenzhen Res Inst Shenzhen 518057 Peoples R China

In this paper, a self-propagating video segmentation approach based on patch matching and enhanced Onecut is proposed, which takes full advantage of the target's color feature, shape feature, and motion information. Firstly, an interactive key frame segmentation is performed to obtain the accurate initial contour of target. Secondly, some sampling patches are uniformly selected along the target contour of previous frame to initialize the localized classifiers. Afterwards, the patch matching is used to pass this contour to the current frame. Simultaneously, the localized classifiers are moved to current frame similarly, and their corresponding positions and parameters are also updated. Eventually, the foreground and background probability maps of current frame are calculated through the localized classifiers as well as global probability models, and then the enhanced Onecut model is constructed to obtain its segmentation result. Compared with the state-of-the-art video segmentation methods, our proposed approach performs outstandingly on the DAVIS dataset.

关键词： Video segmentation Localized classifier Patch matching Enhanced Onecut

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Utility-Oriented Underwater image Quality Assessment Based on Transfer Learning

arXiv

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arXiv 2022年

作者： Chen, Weiling Lin, Rongfu Liao, Honggang Zhao, Tiesong Gu, Ke Le Callet, Patrick Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China Peng Cheng Laboratory Shenzhen518000 China The Faculty of Information Technology Beijing University of Technology Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Équipe Image Perception et Interaction Laboratoire des Sciences du Numérique de Nantes Université de Nantes France

The widespread image applications have greatly promoted the vision-based tasks, in which the image Quality Assessment (IQA) technique has become an increasingly significant issue. For user enjoyment in multimedia systems, the IQA exploits image fidelity and aesthetics to characterize user experience;while for other tasks such as popular object recognition, there exists a low correlation between utilities and perceptions. In such cases, the fidelity-based and aesthetics-based IQA methods cannot be directly applied. To address this issue, this paper proposes a utility-oriented IQA in object recognition. In particular, we initialize our research in the scenario of underwater fish detection, which is a critical task that has not yet been perfectly addressed. Based on this task, we build an Underwater image Utility Database (UIUD) and a learning-based Underwater image Utility Measure (UIUM). Inspired by the top-down design of fidelity-based IQA, we exploit the deep models of object recognition and transfer their features to our UIUM. Experiments validate that the proposed transfer-learning-based UIUM achieves promising performance in the recognition task. We envision our research provides insights to bridge the researches of IQA and computer vision. Copyright © 2022, The Authors. All rights reserved.

关键词： Multimedia systems

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Predefined-time optimization for distributed resource allocation

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2020年第16期357卷 11323-11348页

作者： Lin, Wen-Ting Wang, Yan-Wu Li, Chaojie Yu, Xinghuo Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan Peoples R China RMIT Univ Sch Engn Melbourne Vic 3000 Australia

To meet certain quality and safety standards, convergence in predefined time to the optimal solution of optimization problems is always sought in many applications. In this paper, a novel distributed predefined-time convergent algorithm is proposed for the resource allocation problem. A distributed parameter learning method is introduced, which guarantees the fully distributed characterization of the proposed algorithm. Specifically, by employing nonhomogeneous functions with exponential terms, the proposed algorithm can achieve a predefined-time convergence rate, which further allows the convergence time to be a user-defined parameter. The proposed algorithm is faster than the asymptotically convergent and exponentially convergent algorithms and current fixed-time convergent algorithms. Moreover, with the convergence time of the proposed algorithm being an implicit parameter of the system, it can achieve convergence in any predefined time with properly-chosen system parameters, which contributes to the fast convergence of the proposed algorithm. Application to the power dispatch problem verifies the result, which demonstrates that the convergence rate of the proposed algorithm far outweighs that of current fixed-time convergent algorithms. (C) 2019 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

关键词： Electric load dispatching

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Metabolic Efficiency Improvement of Human Walking by Shoulder Stress Reduction through Load Transfer Backpack

Metabolic Efficiency Improvement of Human Walking by Shoulde...

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IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Cao, Yu Huang, Jian Li, Xiaolong Zhang, Mengshi Xiong, Caihua Mohammed, Samer Zhu, Yaonan Hasegawa, Yasuhisa Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China UPEC LISSI F-94400 Vitry Sur Seine France Nagoya Univ Dept Micronano Mech Sci & Engn Nagoya Aichi 4648603 Japan

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

The dynamic load attached to the load gravity imposes an excessive burden to human shoulders during load carriage, resulting in possible muscle injuries and additional physical exertion. This paper proposes an active suspension backpack, capable of transferring partial load from human shoulders to pelvis and alleviating the dynamic load through separated panels and motor actuation, to reduce pressure on human shoulders and improve walking metabolic efficiency. Based on the human body motion in the vertical direction, the dynamical model of the human-backpack system with shoulder interaction force measured by a soft ballonet with an embedded air pressure sensor is introduced, and an impedance controller has been implemented to maintain a relatively small and constant pressure on the shoulder. In an experimental case study, we presents preliminary results of three healthy subjects performing a treadmill walking with a 20kg load in ACTIVE configuration where the shoulder pressure shows a decrease by 30% along with a reduction of the metabolic energy consumption by 16.4%, compared with the load LOCKED case.

关键词： Legged locomotion Pressure sensors Muscles Pressure measurement Impedance Pelvis Motion measurement

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Network resilience

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PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS 2022年 971卷 1-108页

作者： Liu, Xueming Li, Daqing Ma, Manqing Szymanski, Boleslaw K. Stanley, H. Eugene Gao, Jianxi Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China Civil Aviat Univ China Coll Safety Sci & Engn Tianjin 300300 Peoples R China Rensselaer Polytech Inst Dept Comp Sci Troy NY 12180 USA Network Sci Rensselaer Polytech Inst Technol Ctr Troy NY 12180 USA Boston Univ Ctr Polymer Studies Dept Phys Boston MA 02215 USA

Many systems on our planet shift abruptly and irreversibly from the desired state to an undesired state when forced across a "tipping point". Some examples are mass extinctions within ecosystems, cascading failures in infrastructure systems, and changes in human and animal social networks. The ability to avoid such regime shifts or to recover quickly from such a non-resilient state demonstrates a system's resilience;system resilience is a quality that enables a system to adjust its activities to retain its basic functionality when errors and failures occur. In the past 50 years, attention has been paid almost exclusively to low-dimensional systems;scholars have focused on the calibration of the resilience functions of such systems and the identification of indicators of early warning signals based on two to three connected components. In recent years, taking advantage of network theory and the availability of lavish real datasets, network scientists have begun to explore real-world complex networked multidimensional systems, as well as their resilience functions and early warning indicators. This report presents a comprehensive review of resilience functions and regime shifts in complex systems in domains such as ecology, biology, society, and infrastructure. The research approach includes empirical observations, experimental studies, mathematical modeling, and theoretical analysis. We also review the definitions of some ambiguous terms, including robustness, resilience, and stability.

关键词： Complex networks Resilience Nonlinear dynamics Alternative stable states Tipping points Phase transitions

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Multi-level Temporal Pyramid Network for Action Detection 3rd

Multi-level Temporal Pyramid Network for Action Detection

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3rd Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Wang, Xiang Gao, Changxin Zhang, Shiwei Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Alibaba Grp DAMO Acad Hangzhou Peoples R China

ISBN: (纸本)9783030606381;9783030606398

Currently, one-stage frameworks have been widely applied for temporal action detection, but they still suffer from the challenge that the action instances span a wide range of time. The reason is that these one-stage detectors, e.g., Single Shot Multi-Box Detector (SSD), extract temporal features only applying a single-level layer for each head, which is not discriminative enough to perform classification and regression. In this paper, we propose a Multi-Level Temporal Pyramid Network (MLTPN) to improve the discrimination of the features. Specially, we first fuse the features from multiple layers with different temporal resolutions, to encode multi-layer temporal information. We then apply a multi-level feature pyramid architecture on the features to enhance their discriminative abilities. Finally, we design a simple yet effective feature fusion module to fuse the multi-level multi-scale features. By this means, the proposed MLTPN can learn rich and discriminative features for different action instances with different durations. We evaluate MLTPN on two challenging datasets: THUMOS'14 and Activitynet v1.3, and the experimental results show that MLTPN obtains competitive performance on Activitynet v1.3 and outperforms the state-of-the-art approaches on THUMOS'14 significantly.

关键词： Action detection One-stage Feature

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Attribute-specific control units in StyleGAN for fine-grained image manipulation

arXiv

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arXiv 2021年

作者： Wang, Rui Chen, Jian Yu, Gang Sun, Li Yu, Changqian Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Tencent Shanghai China East China Normal University Shanghai China

image manipulation with StyleGAN has been an increasing concern in recent years. Recent works have achieved tremendous success in analyzing several semantic latent spaces to edit the attributes of the generated images. However, due to the limited semantic and spatial manipulation precision in these latent spaces, the existing endeavors are defeated in fine-grained StyleGAN image manipulation, i.e., local attribute translation. To address this issue, we discover attribute-specific control units, which consist of multiple channels of feature maps and modulation styles. Specifically, we collaboratively manipulate the modulation style channels and feature maps in control units rather than individual ones to obtain the semantic and spatial disentangled controls. Furthermore, we propose a simple yet effective method to detect the attribute-specific control units. We move the modulation style along a specific sparse direction vector and replace the filter-wise styles used to compute the feature maps to manipulate these control units. We evaluate our proposed method in various face attribute manipulation tasks. Extensive qualitative and quantitative results demonstrate that our proposed method performs favorably against the state-of-the-art methods. The manipulation results of real images further show the effectiveness of our method. © 2021, CC BY.

关键词： Modulation

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Flocking of uncertain nonlinear multi-agent systems via distributed adaptive event-triggered control

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NEUROCOMPUTING 2021年 465卷 503-513页

作者： Zou, Yong An, Qing Miao, Suoxia Chen, Shiming Wang, Xiaoming Su, Housheng East China Jiaotong Univ Sch Elect & Automat Engn Nanchang 330013 Jiangxi Peoples R China Wuchang Univ Technol Artificial Intelligence Sch Wuhan 430223 Peoples R China Nanchang Inst Technol Sch Sci Nanchang 330099 Jiangxi Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Image Proc & Intelligent Control Key Lab Educ Minist China Luoyu Rd 1037 Wuhan 430074 Peoples R China Guangdong HUST Ind Technol Res Inst Guangdong Prov Key Lab Digital Mfg Equipment Dongguan 523808 Peoples R China

This paper investigates flocking problems with uncertain nonlinear multi-agent systems. Two distributed adaptive event-triggered control algorithms are proposed for leaderless and leader-follower flocking, separately. According to Lyapunov stability theory, under the proposed control protocols, we obtain some sufficient conditions to ensure stable flocking motion and maintain connectivity of network topology. And it is proven that there is a positive lower limit of the interval between any two trigger events, which indicates that the Zeno behavior will not occur. As a result, communication bandwidth resources are effectively saved on the premise of stable flocking. Lastly, numerical simulations are presented to verify the availability of the theoretical results. CO 2021 Elsevier B.V. All rights reserved.

关键词： Adaptive control Event-triggered control Flocking Multi-agent systems

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FlyIT: Drosophila Embryogenesis image Annotation based on image Tiling and Convolutional Neural Networks

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021年第1期18卷 194-204页

作者： Long, Wei Li, Tiange Yang, Yang Shen, Hong-Bin Shanghai Jiao Tong Univ Ctr Brain Comp & Machine Intelligence Dept Comp Sci & Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Key Lab Shanghai Educ Commiss Intelligent Interact & Cogn Minhang 200240 Peoples R China Shanghai Jiao Tong Univ Brain Sci & Technol Res Ctr Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China

With the rise of image-based transcriptomics, spatial gene expression data has become increasingly important for understanding gene regulations from the tissue level down to the cell level. Especially, the gene expression images of Drosophila embryos provide a new data source in the study of Drosophila embryogenesis. It is imperative to develop automatic annotation tools since manual annotation is labor-intensive and requires professional knowledge. Although a lot of image annotation methods have been proposed in the computer vision field, they may not work well for gene expression images, due to the great difference between these two annotation tasks. Besides the apparent difference on images, the annotation is performed at the gene level rather than the image level, where the expression patterns of a gene are recorded in multiple images. Moreover, the annotation terms often correspond to local expression patterns of images, yet they are assigned collectively to groups of images and the relations between the terms and single images are unknown. In order to learn the spatial expression patterns comprehensively for genes, we propose a new method, called FlyIT (image annotation based on image Tiling and convolutional neural networks for fruit fly). We implement two versions of FlyIT, learning at image-level and gene-level, respectively. The gene-level version employs an image tiling strategy to get a combined image feature representation for each gene. FlyIT uses a pre-trained ResNet model to obtain feature representation and a new loss function to deal with the class imbalance problem. As the annotation of Drosophila images is a multi-label classification problem, the new loss function considers the difficulty levels for recognizing different labels of the same sample and adjusts the sample weights accordingly. The experimental results on the FlyExpress database show that both the image tiling strategy and the deep architecture lead to the great enhancement of the an

关键词： Biological image understanding convolutional neural networks drosophila embryo gene expression image

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Event-Based Synchronization for Multiple Neural Networks With Time Delay and Switching Disconnected Topology

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第12期51卷 5993-6003页

作者： Chen, Jiejie Chen, Boshan Zeng, Zhigang Jiang, Ping Hubei Normal Univ Sch Comp & Informat Engn Huangshi 435002 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China Hubei Normal Univ Coll Math & Stat Huangshi 435002 Hubei Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Hubei PolyTech Univ Comp Sch Huangshi 435002 Hubei Peoples R China

This article discusses the synchronization problem for a class of multiple delayed neural networks (MDNNs) with a directed switching topology by using an event-triggering strategy. First, a new differential inequality with delay is shown, which is a generalization of Halanay-type inequalities. Then, the sufficient conditions of event-based synchronization (quasisynchronization) for MDNN with sequentially connected topology are obtained by using this inequality and the iterative method. Meantime, we prove that Zeno behavior can be avoided under the designed event-triggering rules. As an extension, MDNN with jointly connected topology is also discussed. Finally, a numerical example is listed to illustrate the results in theory analysis.

关键词： Topology Switches Synchronization Network topology Artificial neural networks Delays Protocols Event-triggered multiple neural networks (MNNs) quasisynchronization switching topology synchronization

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