Updated on 2023/01/06

写真a

 
LU Huimin
 
Scopus Paper Info  
Total Paper Count: 0  Total Citation Count: 0  h-index: 36

Citation count denotes the number of citations in papers published for a particular year.

Affiliation
Faculty of Engineering Department of Mechanical and Control Engineering
Job
Associate Professor
E-mail
メールアドレス
External link

Research Interests

  • Artificial Intelligence

  • Robotics

  • Oceanic Optics

  • Computer Vision

Research Areas

  • Manufacturing Technology (Mechanical Engineering, Electrical and Electronic Engineering, Chemical Engineering) / Measurement engineering

  • Informatics / Perceptual information processing

Degree

  • Kyushu Institute of Technology  -  Doctor of Engineering   2014.03

Biography in Kyutech

  • 2019.09
     

    Kyushu Institute of Technology   Faculty of Engineering   Department of Mechanical and Control Engineering   Associate Professor  

Academic Society Memberships

  • 2020.04   電子情報通信学会   Japan

  • 2019.08   SPIE   United States

  • 2012.01   IEEE   United States

Papers

  • Face Illumination Transfer and Swapping via Dense Landmark and Semantic Parsing Reviewed

    Jin X., Li Z., Ning N., Lu H., Li X., Zhang X., Zhu X., Fang X.

    IEEE Sensors Journal   22 ( 18 )   17391 - 17398   2022.09

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    Language:English   Publishing type:Research paper (scientific journal)

    DOI: 10.1109/JSEN.2020.3025918

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  • Improving the Accuracy of Road Surface Distinction Based on Reflection Intensity Variations Using Ultrasonic Sensor Reviewed

    Nakashima S., Arimura H., Yamamoto M., Mu S., Lu H.

    IEEE Sensors Journal   22 ( 18 )   17399 - 17405   2022.09

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    Language:English   Publishing type:Research paper (scientific journal)

    DOI: 10.1109/JSEN.2020.3033015

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  • Query-based black-box attack against medical image segmentation model Reviewed

    Li S., Huang G., Xu X., Lu H.

    Future Generation Computer Systems   133   331 - 337   2022.08

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    Language:English   Publishing type:Research paper (scientific journal)

    DOI: 10.1016/j.future.2022.03.008

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  • Three-dimensional Object Detection Algorithm Based on Deep Neural Networks for Automatic Driving Reviewed

    Lu H., Yang S.

    Beijing Gongye Daxue Xuebao/Journal of Beijing University of Technology   48 ( 6 )   589 - 597   2022.06

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    Language:Chinese   Publishing type:Research paper (scientific journal)

    DOI: 10.11936/bjutxb2021100027

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  • Cognitive Memory-Guided AutoEncoder for Effective Intrusion Detection in Internet of Things Reviewed

    Lu H., Wang T., Xu X., Wang T.

    IEEE Transactions on Industrial Informatics   18 ( 5 )   3358 - 3366   2022.05

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    Language:English   Publishing type:Research paper (scientific journal)

    DOI: 10.1109/TII.2021.3102637

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  • Learning Cross-Modal Common Representations by Private-Shared Subspaces Separation Reviewed

    Xu X., Lin K., Gao L., Lu H., Shen H.T., Li X.

    IEEE Transactions on Cybernetics   52 ( 5 )   3261 - 3275   2022.05

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    DOI: 10.1109/TCYB.2020.3009004

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  • Answer Again: Improving VQA with Cascaded-Answering Model Reviewed

    Peng L., Yang Y., Zhang X., Ji Y., Lu H., Shen H.T.

    IEEE Transactions on Knowledge and Data Engineering   34 ( 4 )   1644 - 1655   2022.04

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    Language:English   Publishing type:Research paper (scientific journal)

    DOI: 10.1109/TKDE.2020.2998805

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  • Multifeature Fusion-Based Object Detection for Intelligent Transportation Systems Reviewed

    Yang S., Lu H., Li J.

    IEEE Transactions on Intelligent Transportation Systems   2022.01

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    Language:English   Publishing type:Research paper (scientific journal)

    DOI: 10.1109/TITS.2022.3155488

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  • Study on the Learning in Intelligent Control Using Neural Networks Based on Back-Propagation and Differential Evolution Reviewed

    Mu S., Shibata S., Lu H., Yamamoto T., Nakashima S., Tanaka K.

    EAI/Springer Innovations in Communication and Computing   17 - 29   2022.01

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    Language:English   Publishing type:Research paper (international conference proceedings)

    DOI: 10.1007/978-3-030-70451-3_2

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  • Integrity assessment of corroded oil and gas pipelines using machine learning: A systematic review Reviewed

    Soomro A.A., Mokhtar A.A., Kurnia J.C., Lashari N., Lu H., Sambo C.

    Engineering Failure Analysis   131   2022.01

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    Language:English   Publishing type:Article, review, commentary, editorial, etc. (scientific journal)

    DOI: 10.1016/j.engfailanal.2021.105810

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  • Global-PBNet: A Novel Point Cloud Registration for Autonomous Driving Reviewed

    Zheng Y., Li Y., Yang S., Lu H.

    IEEE Transactions on Intelligent Transportation Systems   2022.01

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    DOI: 10.1109/TITS.2022.3153133

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  • FBSNet: A Fast Bilateral Symmetrical Network for Real-Time Semantic Segmentation Reviewed

    Gao G., Xu G., Li J., Yu Y., Lu H., Yang J.

    IEEE Transactions on Multimedia   2022.01

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    Language:English   Publishing type:Research paper (scientific journal)

    DOI: 10.1109/TMM.2022.3157995

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  • Cross-Modal Dynamic Networks for Video Moment Retrieval with Text Query Reviewed

    Wang G., Xu X., Shen F., Lu H., Ji Y., Shen H.T.

    IEEE Transactions on Multimedia   24   1221 - 1232   2022.01

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    Language:English   Publishing type:Research paper (scientific journal)

    DOI: 10.1109/TMM.2022.3142420

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  • Towards Secure and Privacy-Preserving Data Sharing for COVID-19 Medical Records: A Blockchain-Empowered Approach Reviewed

    Tan L., Yu K., Shi N., Yang C., Wei W., Lu H.

    IEEE Transactions on Network Science and Engineering   9 ( 1 )   271 - 281   2022.01

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    Language:English   Publishing type:Research paper (scientific journal)

    DOI: 10.1109/TNSE.2021.3101842

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  • Multidimensional Deformable Object Manipulation Based on DN-Transporter Networks Reviewed

    Teng Y., Lu H., Li Y., Kamiya T., Nakatoh Y., Serikawa S., Gao P.

    IEEE Transactions on Intelligent Transportation Systems   2022.01

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    Language:English   Publishing type:Research paper (scientific journal)

    DOI: 10.1109/TITS.2022.3168303

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  • Generalizable Crowd Counting via Diverse Context Style Learning Reviewed

    Zhao W., Wang M., Liu Y., Lu H., Xu C., Yao L.

    IEEE Transactions on Circuits and Systems for Video Technology   2022.01

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    DOI: 10.1109/TCSVT.2022.3146459

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  • Joint Semantic-Instance Segmentation Method for Intelligent Transportation System Reviewed

    Li Y., Cai J., Zhou Q., Lu H.

    IEEE Transactions on Intelligent Transportation Systems   2022.01

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    DOI: 10.1109/TITS.2022.3190369

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  • Context-Patch Representation Learning with Adaptive Neighbor Embedding for Robust Face Image Super-Resolution Reviewed

    Gao G., Yu Y., Lu H., Yang J., Yue D.

    IEEE Transactions on Multimedia   2022.01

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    Language:English   Publishing type:Research paper (scientific journal)

    DOI: 10.1109/TMM.2022.3192769

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  • Learning Latent Dynamics for Autonomous Shape Control of Deformable Object Reviewed

    Lu H., Teng Y., Li Y.

    IEEE Transactions on Intelligent Transportation Systems   2022.01

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    Language:English   Publishing type:Research paper (scientific journal)

    DOI: 10.1109/TITS.2022.3225322

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  • 3D object detection using improved PointRCNN Reviewed

    Fukitani K., Shin I., Lu H., Yang S., Kamiya T., Nakatoh Y., Serikawa S.

    Cognitive Robotics   2   242 - 254   2022.01

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    DOI: 10.1016/j.cogr.2022.12.001

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  • Robotic Grasp Detection for Parallel Grippers: A Review Reviewed

    Yin Z., Li Y., Cai J., Lu H.

    Proceedings - 2022 IEEE 46th Annual Computers, Software, and Applications Conference, COMPSAC 2022   1184 - 1187   2022.01

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    Language:English   Publishing type:Research paper (international conference proceedings)

    DOI: 10.1109/COMPSAC54236.2022.00186

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  • Grasp Position Estimation from Depth Image Using Stacked Hourglass Network Structure Reviewed

    Hamamoto K., Lu H., Li Y., Kamiya T., Nakatoh Y.O., Serikawa S.

    Proceedings - 2022 IEEE 46th Annual Computers, Software, and Applications Conference, COMPSAC 2022   1188 - 1192   2022.01

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    Language:English   Publishing type:Research paper (international conference proceedings)

    DOI: 10.1109/COMPSAC54236.2022.00187

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  • Edge Computing with Complementary Capsule Networks for Mental State Detection in Underground Mining Industry Reviewed

    Wang M., Wang J., Li Y., Lu H.

    IEEE Transactions on Industrial Informatics   2022.01

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    Language:English   Publishing type:Research paper (scientific journal)

    DOI: 10.1109/TII.2022.3218839

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  • A Two-Phase Learning-Based Swarm Optimizer for Large-Scale Optimization Reviewed

    Lan R., Zhu Y., Lu H., Liu Z., Luo X.

    IEEE Transactions on Cybernetics   51 ( 12 )   6284 - 6293   2021.12

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    In this article, a simple yet effective method, called a two-phase learning-based swarm optimizer (TPLSO), is proposed for large-scale optimization. Inspired by the cooperative learning behavior in human society, mass learning and elite learning are involved in TPLSO. In the mass learning phase, TPLSO randomly selects three particles to form a study group and then adopts a competitive mechanism to update the members of the study group. Then, we sort all of the particles in the swarm and pick out the elite particles that have better fitness values. In the elite learning phase, the elite particles learn from each other to further search for more promising areas. The theoretical analysis of TPLSO exploration and exploitation abilities is performed and compared with several popular particle swarm optimizers. Comparative experiments on two widely used large-scale benchmark datasets demonstrate that the proposed TPLSO achieves better performance on diverse large-scale problems than several state-of-the-art algorithms.

    DOI: 10.1109/TCYB.2020.2968400

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  • DRRS-BC: Decentralized Routing Registration System Based on Blockchain Reviewed

    Lu H., Tang Y., Sun Y.

    IEEE/CAA Journal of Automatica Sinica   8 ( 12 )   1868 - 1876   2021.12

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    The border gateway protocol (BGP) has become the indispensible infrastructure of the Internet as a typical inter-domain routing protocol. However, it is vulnerable to misconfigurations and malicious attacks since BGP does not provide enough authentication mechanism to the route advertisement. As a result, it has brought about many security incidents with huge economic losses. Exiting solutions to the routing security problem such as S-BGP, So-BGP, Ps-BGP, and RPKI, are based on the Public Key Infrastructure and face a high security risk from the centralized structure. In this paper, we propose the decentralized blockchain-based route registration framework-decentralized route registration system based on blockchain (DRRS-BC). In DRRS-BC, we produce a global transaction ledge by the information of address prefixes and autonomous system numbers between multiple organizations and ASs, which is maintained by all blockchain nodes and further used for authentication. By applying blockchain, DRRS-BC perfectly solves the problems of identity authentication, behavior authentication as well as the promotion and deployment problem rather than depending on the authentication center. Moreover, it resists to prefix and subprefix hijacking attacks and meets the performance and security requirements of route registration.

    DOI: 10.1109/JAS.2021.1004204

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  • CAA: Candidate-Aware Aggregation for Temporal Action Detection Reviewed

    Ren Y., Xu X., Shen F., Yao Y., Lu H.

    MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia   4930 - 4938   2021.10

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    DOI: 10.1145/3474085.3475616

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  • Robust Facial Image Super-Resolution by Kernel Locality-Constrained Coupled-Layer Regression Reviewed

    Gao G., Zhu D., Lu H., Yu Y., Chang H., Yue D.

    ACM Transactions on Internet Technology   21 ( 3 )   2021.08

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    Super-resolution methods for facial image via representation learning scheme have become very effective methods due to their efficiency. The key problem for the super-resolution of facial image is to reveal the latent relationship between the low-resolution (LR) and the corresponding high-resolution (HR) training patch pairs. To simultaneously utilize the contextual information of the target position and the manifold structure of the primitive HR space, in this work, we design a robust context-patch facial image super-resolution scheme via a kernel locality-constrained coupled-layer regression (KLC2LR) scheme to obtain the desired HR version from the acquired LR image. Here, KLC2LR proposes to acquire contextual surrounding patches to represent the target patch and adds an HR layer constraint to compensate the detail information. Additionally, KLC2LR desires to acquire more high-frequency information by searching for nearest neighbors in the HR sample space. We also utilize kernel function to map features in original low-dimensional space into a high-dimensional one to obtain potential nonlinear characteristics. Our compared experiments in the noisy and noiseless cases have verified that our suggested methodology performs better than many existing predominant facial image super-resolution methods.

    DOI: 10.1145/3418462

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  • A Hybrid Feature Selection Algorithm Based on a Discrete Artificial Bee Colony for Parkinson's Diagnosis Reviewed

    Li H., Pun C.M., Xu F., Pan L., Zong R., Gao H., Lu H.

    ACM Transactions on Internet Technology   21 ( 3 )   2021.08

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    Parkinson's disease is a neurodegenerative disease that affects millions of people around the world and cannot be cured fundamentally. Automatic identification of early Parkinson's disease on feature data sets is one of the most challenging medical tasks today. Many features in these datasets are useless or suffering from problems like noise, which affect the learning process and increase the computational burden. To ensure the optimal classification performance, this article proposes a hybrid feature selection algorithm based on an improved discrete artificial bee colony algorithm to improve the efficiency of feature selection. The algorithm combines the advantages of filters and wrappers to eliminate most of the uncorrelated or noisy features and determine the optimal subset of features. In the filter, three different variable ranking methods are employed to pre-rank the candidate features, then the population of artificial bee colony is initialized based on the significance degree of the re-rank features. In the wrapper part, the artificial bee colony algorithm evaluates individuals (feature subsets) based on the classification accuracy of the classifier to achieve the optimal feature subset. In addition, for the first time, we introduce a strategy that can automatically select the best classifier in the search framework more quickly. By comparing with several publicly available datasets, the proposed method achieves better performance than other state-of-the-art algorithms and can extract fewer effective features.

    DOI: 10.1145/3397161

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  • Aspect-Based Sentiment Analysis of User Reviews in 5G Networks Reviewed

    Zhang Y., Lu H., Jiang C., Li X., Tian X.

    IEEE Network   35 ( 4 )   228 - 233   2021.07

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    Aspect-based sentiment analysis can help consumers provide clear and objective sentiment recommendations through massive amounts of data and is conducive to overcoming ambiguous human weaknesses in subjective judgments. However, the robustness and accuracy of existing sentiment analysis methods must still be improved. In this article, deep learning and machine learning techniques are combined to construct a sentiment analysis model based on ensemble learning ideas. Furthermore, the proposed model is applied to a sentiment classification for user reviews about restaurants, which are the representative location-based and user-oriented applications in 5G networks. Specifically, a multi-aspect-labeling model is established, and an ensemble aspect-based model is proposed based on the concept of ensemble learning to predict the consumer's true consumption feelings and willingness to consume again, and to improve machine learning based on the developed model. The predictive performance of the algorithm lies within a single domain.

    DOI: 10.1109/MNET.011.2000400

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  • Multi-Aspect Aware Session-Based Recommendation for Intelligent Transportation Services Reviewed

    Zhang Y., Li Y., Wang R., Hossain M.S., Lu H.

    IEEE Transactions on Intelligent Transportation Systems   22 ( 7 )   4696 - 4705   2021.07

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    In the intelligent transportation system, the session data usually represents the users' demand. However, the traditional approaches only focus on the sequence information or the last item clicked by the user, which cannot fully represent user preferences. To address this issue, this paper proposes an Multi-aspect Aware Session-based Recommendation (MASR) model for intelligent transportation services, which comprehensively considers the user's personalized behavior from multiple aspects. In addition, it developed a concise and efficient transformer-style self-attention to analyze the sequence information of the current session, for accurately grasping the user's intention. Finally, the experimental results show that MASR is available to improve user satisfaction with more accurate and rapid recommendations, and reduce the number of user operations to decrease the safety risk during the transportation service.

    DOI: 10.1109/TITS.2020.2990214

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  • A Model for Joint Planning of Production and Distribution of Fresh Produce in Agricultural Internet of Things Reviewed

    Han J., Lin N., Ruan J., Wang X., Wei W., Lu H.

    IEEE Internet of Things Journal   8 ( 12 )   9683 - 9696   2021.06

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    The production and distribution planning of fresh produce is a complex optimization problem, which is affected by many factors, including its perishable characteristics. Farmers cannot guarantee the efficiency and accuracy of production and distribution decisions. Given the close relationship between the production and distribution of annual fresh produce, the intention of our research is to solve the two-stage joint planning problem and maximize the revenue of farmers ultimately. The internal relationship matrix between the two links of production and distribution is established. On this basis, we propose a mixed-integer programming (MIP) model, which covers the constraints of labor and capital. The decisions obtained are not only based on price estimation and resource availability but also on the impact of the agricultural Internet-of-Things technology and the special requirements of each distribution channel. Numerical experiments demonstrate that when the planting area is 1, 4, and 6 ha, the proposed joint planning model can improve the distribution revenue of farmers by 7.92%, 4.15%, and 4.94%, respectively, compared with the traditional separate decision-making approach of distribution. According to different decision scenarios, management insights have been obtained. For example, farmers should carefully sort and package products as well as choose a timely and safe third-party express delivery company. Additionally, the proposed strategy can evaluate the impact of distribution channels on farmers' revenue.

    DOI: 10.1109/JIOT.2020.3037729

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  • CT temporal subtraction: Techniques and clinical applications Reviewed

    Aoki T., Kamiya T., Lu H., Terasawa T., Ueno M., Hayashida Y., Murakami S., Korogi Y.

    Quantitative Imaging in Medicine and Surgery   11 ( 6 )   2021.06

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    DOI: 10.21037/qims-20-1367

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  • User-Oriented Virtual Mobile Network Resource Management for Vehicle Communications Reviewed

    Lu H., Zhang Y., Li Y., Jiang C., Abbas H.

    IEEE Transactions on Intelligent Transportation Systems   22 ( 6 )   3521 - 3532   2021.06

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    Currently, advanced communications and networks greatly enhance user experiences and have a major impact on all aspects of people's lifestyles in terms of work, society, and the economy. However improving competitiveness and sustainable vehicle network services, such as higher user experience, considerable resource utilization and effective personalized services, is a great challenge. Addressing these issues, this paper proposes a virtual network resource management based on user behavior to further optimize the existing vehicle communications. In particular, ensemble learning is implemented in the proposed scheme to predict the user's voice call duration and traffic usage for supporting user-centric mobile services optimization. Sufficient experiments show that the proposed scheme can significantly improve the quality of services and experiences and that it provides a novel idea for optimizing vehicle networks.

    DOI: 10.1109/TITS.2020.2991766

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  • Output-Bounded and RBFNN-Based Position Tracking and Adaptive Force Control for Security Tele-Surgery Reviewed

    Wang T., Ji X., Song A., Madani K., Chohra A., Lu H., Monero R.

    ACM Transactions on Multimedia Computing, Communications and Applications   17 ( 2s )   2021.06

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    In security e-health brain neurosurgery, one of the important processes is to move the electrocoagulation to the appropriate position in order to excavate the diseased tissue.1 However, it has been problematic for surgeons to freely operate the electrocoagulation, as the workspace is very narrow in the brain. Due to the precision, vulnerability, and important function of brain tissues, it is essential to ensure the precision and safety of brain tissues surrounding the diseased part. The present study proposes the use of a robot-assisted tele-surgery system to accomplish the process. With the aim to achieve accuracy, an output-bounded and RBF neural network-based bilateral position control method was designed to guarantee the stability and accuracy of the operation process. For the purpose of accomplishing a minimal amount of bleeding and damage, an adaptive force control of the slave manipulator was proposed, allowing it to be appropriate to contact the susceptible vessels, nerves, and brain tissues. The stability was analyzed, and the numerical simulation results revealed the high performance of the proposed controls.

    DOI: 10.1145/3394920

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  • ATTDC: An Active and Traceable Trust Data Collection Scheme for Industrial Security in Smart Cities Reviewed

    Shen M., Liu A., Huang G., Xiong N.N., Lu H.

    IEEE Internet of Things Journal   8 ( 8 )   6437 - 6453   2021.04

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    With billions of sensing devices are deployed in smart cities to monitor regions of interests and collect large sensing data, the Internet-of-Things (IoT) applications are being widely used in various fields and empower the intelligent smart cities. Due to the smart decision made by IoT applications depends on the reliability of data collection, it is pivotal to collect data from the trust sensing devices. However, how to identify the credibility of sensor nodes to ensure the credibility of data collection is a challenge issue. In this article, an active and traceable trust-based data collection (ATTDC) scheme is proposed to collect trust data in Internet of Thing. The main contribution of this article are as follows: 1) an active trust framework is proposed to quickly obtain the trustworthiness of sensor nodes by using unmanned aerial vehicles (UAVs) with a piggybacking method; 2) in order to accurately obtain the trust degree of the sensor nodes, a traceable trust method of obtaining is proposed in which nodes in the network send data packets by digital signature, Tracing suspicious nodes according to data routing paths to obtain active trust, therefore, the acquisition cost of the network can be effectively reduced; and 3) In order to reduce the acquisition cost of UAV, an ant colony algorithm-based flight path algorithm is designed to reduce the flight path of UAV, and obtain the credibility evaluation of as many nodes as possible. The experimental results show that the ATTDC scheme proposed in this article can identify the trust of the sensing nodes faster and more accurately, ensuring the credibility of data collection.

    DOI: 10.1109/JIOT.2021.3049173

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  • A supervoxel classification based method for multi-organ segmentation from abdominal ct images Reviewed

    Wu J., Li G., Lu H., Kamiya T.

    Journal of Image and Graphics(United Kingdom)   9 ( 1 )   9 - 14   2021.03

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    Multi-organ segmentation is a critical step in Computer-Aided Diagnosis (CAD) system. We proposed a novel method for automatic abdominal multi-organ segmentation by introducing spatial information in the process of supervoxel classification. Supervoxels with boundaries adjacent to anatomical edges are separated from the image by using the Simple Linear Iterative Clustering (SLIC) from the images. Then a random forest classifier is built to predict the labels of the supervoxels according to their spatial and intensity features. Thirty abdominal CT images are used in the experiment of segmentation task for spleen, right kidney, left kidney, and liver region. The experiment result shows that the proposed method achieves a higher accuracy of segmentation compares to our previous model-based method.

    DOI: 10.18178/joig.9.1.9-14

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  • 5G-Network-Enabled Smart Ambulance: Architecture, Application, and Evaluation Reviewed

    Zhai Y., Xu X., Chen B., Lu H., Wang Y., Li S., Shi X., Wang W., Shang L., Zhao J.

    IEEE Network   35 ( 1 )   190 - 196   2021.03

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    As the fifth generation (5G) network comes to the fore, the realization of 5G-enabled service has attracted much attention from both healthcare academics and practitioners. In particular, 5G-enabled emergency ambulance service allows to connect a patient and an ambulance crew at an accident scene or in transit with the awaiting emergency department team at the destination hospital seamlessly so as to improve the rescue rate of patients. However, the application of the 5G network in ambulance service currently lacks a reliable solution and simulation testing of performance in the existing literature. To achieve this end, the primary aim of this study is to propose a 5G-enabled smart ambulance service and then test the quality of service of the proposed solution in experimental settings. We also consider emergency scenarios to investigate the task completion and accuracy of 5G-enabled smart ambulance, and to verify the superiority of our proposed solution. Our study explores the value of a 5G-en-abled smart ambulance and provides practical insights for 5G network construction, business development, and network optimization of smart ambulance service.

    DOI: 10.1109/MNET.011.2000014

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  • Deep-learning based segmentation algorithm for defect detection in magnetic particle testing images Reviewed

    Ueda A., Lu H., Kamiya T.

    Proceedings of International Conference on Artificial Life and Robotics   2021   235 - 238   2021.01

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    Magnetic Particle Testing (MPT), also referred to as magnetic particle inspection, is a nondestructive examination (NDE) technique used to detect surface and slightly subsurface flaws in most ferromagnetic materials such as iron, nickel, and cobalt, and some of their alloys. In a bad environment, the procedure is complicated, and automation of MPT is strongly desired. To find defects in the formed magnetic powder pattern, it is required to be highly skilled and automation has been considered difficult. In recent years, many defect detection methods based on deep learning have been proposed, and the effectiveness of deep learning has been shown in the task of automatically detecting various types of defects having different shapes and sizes. In this paper, we describe the development of deep learning based segmentation algorithm for defect detection in MPT images. We have achieved a F2 score of 84.04% by using U-Net as the segmentation model and by utilizing a strong backbone network and an optimal loss function.

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  • Sentence pair modeling based on semantic feature map for human interaction with IoT devices Reviewed

    Yu R., Lu W., Lu H., Wang S., Li F., Zhang X., Yu J.

    International Journal of Machine Learning and Cybernetics   2021.01

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    The rapid development of Internet of Things (IoT) brings an urgent requirement on intelligent human–device interactions using natural language, which are critical for facilitating people to use IoT devices. The efficient interactive approaches depend on various natural language understanding technologies. Among them, sentence pair modeling (SPM) is essential, where neural networks have achieved great success in SPM area due to their powerful abilities in feature extraction and representation. However, as sentences are one-dimensional (1D) texts, the available neural networks are usually limited to 1D sequential models, which prevents the performance improvement of SPM task. To address this gap, in this paper, we propose a novel neural architecture for sentence pair modeling, which utilizes 1D sentences to construct multi-dimensional feature maps similar to images containing multiple color channels. Based on the feature maps, more kinds of neural models become applicable on SPM task, including 2D CNN. In the proposed model, first, the sentence on a specific granularity is encoded with BiLSTM to generate the representation on this granularity, which is viewed as a special channel of the sentence. The representations from different granularity are merged together to construct semantic feature map of the input sentence. Then, 2D CNN is employed to encode the feature map to capture the deeper semantic features contained in the sentence. Next, another 2D CNN is utilized to capture the interactive matching features between sentences, followed by 2D max-pooling and attention mechanism to generate the final matching representation. Finally, the matching degree of sentences are judged with a sigmoid function according to the matching representation. Extensive experiments are conducted on two real-world data sets. In comparison with benchmarks, the proposed model achieved remarkable results, and performed better or comparably with BERT-based models. Our work is beneficial to building a more powerful humanized interaction system with IoT devices.

    DOI: 10.1007/s13042-021-01349-x

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  • Improved Point-Voxel Region Convolutional Neural Network: 3D Object Detectors for Autonomous Driving Reviewed

    Li Y., Yang S., Zheng Y., Lu H.

    IEEE Transactions on Intelligent Transportation Systems   2021.01

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    Recently, 3D object detection based on deep learning has achieved impressive performance in complex indoor and outdoor scenes. Among the methods, the two-stage detection method performs the best; however, this method still needs improved accuracy and efficiency, especially for small size objects or autonomous driving scenes. In this paper, we propose an improved 3D object detection method based on a two-stage detector called the Improved Point-Voxel Region Convolutional Neural Network (IPV-RCNN). Our proposed method contains online training for data augmentation, upsampling convolution and k-means clustering for the bounding box to achieve 3D detection tasks from raw point clouds. The evaluation results on the KITTI 3D dataset show that the IPV-RCNN achieved a 96% mAP, which is 3% more accurate than the state-of-the-art detectors.

    DOI: 10.1109/TITS.2021.3071790

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  • Image-Scale-Symmetric Cooperative Network for Defocus Blur Detection Reviewed

    Zhao F., Lu H., Zhao W., Yao L.

    IEEE Transactions on Circuits and Systems for Video Technology   2021.01

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    Defocus blur detection (DBD) for natural images is a challenging vision task especially in the presence of homogeneous regions and gradual boundaries. In this paper, we propose a novel image-scale-symmetric cooperative network (IS2CNet) for DBD. On one hand, in the process of image scales from large to small, IS2CNet gradually spreads the recept of image content. Thus, the homogeneous region detection map can be optimized gradually. On the other hand, in the process of image scales from small to large, IS2CNet gradually feels the high-resolution image content, thereby gradually refining transition region detection. In addition, we propose a hierarchical feature integration and bi-directional delivering mechanism to transfer the hierarchical feature of previous image scale network to the input and tail of the current image scale network for guiding the current image scale network to better learn the residual. The proposed approach achieves state-of-the-art performance on existing datasets. Codes and results are available at: https://github.com/wdzhao123/IS2CNet.

    DOI: 10.1109/TCSVT.2021.3095347

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  • Automatic Classification of Respiratory Sounds using HPSS Reviewed

    MARUBASHI Yuki, ASATANI Naoki, LU Huimin, KAMIYA Tohru, MABU Shingo, KIDO Shoji

    Medical Imaging and Information Sciences ( MEDICAL IMAGING AND INFORMATION SCIENCES )   38 ( 2 )   95 - 100   2021.01

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    <p>Respiratory disease is a serious illness that accounts for three of the top ten causes of death in the world, and approximately eight million people died worldwide each year. Early detection and early treatment are important for the prevention of illness due to these diseases. Currently, auscultation is performed for the diagnosis of respiratory diseases,however there is a problem that quantitative diagnosis is difficult. Therefore, in this paper, we propose a new automatic classification method of respiratory sounds to support the diagnosis of respiratory diseases on auscultation. In the proposed method, respiratory sound data is converted into a spectrogram image by applying the short-time Fourier transform. Then,we apply HPSS (Harmonic/Percussive Sound Separation) algorithm to the respiratory sound spectrogram to separate it into a harmonic spectrogram and a percussive spectrogram. The three generated spectrograms are used for classification of respiratory sounds by CNN (Convolutional Neural Network) and SVM (Support Vector Machine) classifiers. Our proposed method obtained superior classification performance compared to the case without applying HPSS and satisfactory results are obtained.</p>

    DOI: 10.11318/mii.38.95

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  • Generalized Label Enhancement with Sample Correlations Reviewed

    Zheng Q., Zhu J., Tang H., Liu X., Li Z., Lu H.

    IEEE Transactions on Knowledge and Data Engineering   2021.01

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    Recently, label distribution learning (LDL) has drawn much attention in machine learning, where LDL model is learned from labelel instances. Different from single-label and multi-label annotations, label distributions describe the instance by multiple labels with different intensities and accommodate to more general scenes. Since most existing machine learning datasets merely provide logical labels, label distributions are unavailable in many real-world applications. To handle this problem, we propose two novel label enhancement methods, i.e., Label Enhancement with Sample Correlations (LESC) and generalized Label Enhancement with Sample Correlations (gLESC). More specifically, LESC employs a low-rank representation of samples in the feature space, and gLESC leverages a tensor multi-rank minimization to further investigate the sample correlations in both the feature space and label space. Benefitting from the sample correlations, the proposed methods can boost the performance of label enhancement. Extensive experiments on 14 benchmark datasets demonstrate the effectiveness and superiority of our methods.

    DOI: 10.1109/TKDE.2021.3073157

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  • Discrete-Time Predictive Sliding Mode Control for a Constrained Parallel Micropositioning Piezostage Reviewed

    Kang S., Wu H., Yang X., Li Y., Yao J., Chen B., Lu H.

    IEEE Transactions on Systems, Man, and Cybernetics: Systems   2021.01

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    This article proposes a new discrete-time predictive sliding mode control (DPSMC) for a parallel micropositioning piezostage to improve the motion accuracy in the presence of cross-coupling hysteresis nonlinearities and input constraints. Unlike the traditional linear discrete-time sliding mode control (DSMC), the proposed DPSMC is chattering free and has a faster convergence rate thanks to the design of a nonlinear discrete-time fast integral terminal sliding mode surface. Moreover, by combining with the receding horizon optimization, the sliding mode state is predicted to follow the expected trajectory of a predefined continuous sliding mode reaching law, which also allows the proposed controller to explicitly deal with constraints. The stability of the closed-loop system is analyzed under the model disturbances and constraints, and proves that the proposed DPSMC can offer a smaller quasi-sliding mode bandwidth than the traditional DSMC. The effectiveness of the proposed controller is validated by a series of numerical simulations and experiments. Results demonstrate the advantages of proposed DPSMC over the traditional DSMC method.

    DOI: 10.1109/TSMC.2021.3062581

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  • Classification of Respiratory Sounds by scSE-CRNN from Triple Types of Respiratory Sound Images Reviewed

    Asatani Naoki, Lu Huimin, Kamiya Tohru, Mabu Shingo, Kido Shoji

    Medical Imaging and Information Sciences ( MEDICAL IMAGING AND INFORMATION SCIENCES )   38 ( 4 )   152 - 159   2021.01

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    <p>Due to the respiratory diseases such as chronic obstructive pulmonary disease and lower respiratory tract infections nearly 8 million people were died worldwide each year. Reducing the number of deaths from respiratory diseases is a challenge to be solved worldwide. Early detection is the most efficient way to reduce the number of deaths in respiratory illness. As a result, the spread of infection can be suppressed, and the therapeutic effect can be enhanced. Currently, auscultation is performed as a promising method for early detection of respiratory diseases. Auscultation can estimate respiratory diseases by distinguishing abnormal sounds contained in respiratory sounds. However, medical staff need to be trained to perform auscultation with high accuracy. Also, the diagnostic results depend on each staff subjectively, which can lead to inconsistent results. Therefore, in some environments, a shortage of specialized health care workers can lead to the spread of respiratory illness. To solve this problem, an application that analyzes respiratory sounds and outputs diagnostic results is needed. In this paper, we use a newly proposed deep learning model to automatically classify the respiratory sound data from the ICBHI 2017 Challenge Dataset. Short-Time Fourier Transform, Constant-Q Transform, and Continuous Wavelet Transform are applied to the respiratory sound data to convert it into the time-frequency region. Then, the obtained three types of breath sound images are input to CRNN (Convolutional Recurrent Neural Network) having scSE (Spatial and Channel Squeeze & Excitation) Block. The accuracy is improved by weighting the features of each image. As a result, AUC (Area Under the Curve): (Normal:0.87, Crackle:0.88, Wheeze:0.92, Both:0.89), Sensitivity: 0.67, Specificity: 0.82, Average Score: 0.75, Harmonic Score: 0.74, Accuracy: 0.75 were obtained.</p>

    DOI: 10.11318/mii.38.152

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  • Underwater image super-resolution using SRCNN Reviewed

    Ooyama S., Lu H., Kamiya T., Serikawa S.

    Proceedings of SPIE - The International Society for Optical Engineering   11884   2021.01

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    In recent years, energy minerals have become more important due to the rapid industrialization worldwide. Due to the rapid industrialization on a global scale, there is a shortage of mineral resources, and there are more opportunities to rely on alternative energy sources. Therefore, the exploration of marine resources, which are abundant in the ocean, is being promoted. However, it is dangerous and impractical for humans to dive and search for marine resources by hand. Therefore, it is possible to proceed with underwater exploration safely by having a robot do the work instead. Robots have been used as a mainstream search tool in the underwater environment due to the existence of various hazardous environmental conditions. However, there are several problems associated with robot control in underwater environments, one of which is poor visibility in the water. One of the problems is the poor visibility in the water. To improve the visibility in the water, we are trying to increase the resolution of underwater images by using super-resolution technology. In this paper, we conduct experiments using SRCNN, which is a basic super-resolution technique for underwater images. In addition, we investigate the effectiveness of "Mish", which has been attracting attention in recent years for its potential to surpass the performance of "ReLU", although "ReLU"is a typical activation function of neural networks, on SRCNN.

    DOI: 10.1117/12.2603761

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  • Shape restoration by shadow information and photometric stereo Reviewed

    Kameda S., Lu H., Kamiya T., Serikawa S.

    Proceedings of SPIE - The International Society for Optical Engineering   11884   2021.01

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    Virtual Reality (VR) systems are become popular in recent years, and the capture 3D objects from the real world have been studied. 3D objects have large and complex data. In this paper, we propose a novel method that use the shadow information and Photometric stereo with their surface from one point of view to recover the 3D shapes. The experimental results show that the proposed method performs well accuracy.

    DOI: 10.1117/12.2604193

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  • Image quality improvement using local adaptive neighborhood-based dark channel prior Reviewed

    Onoyama T., Lu H., Soomro A.A., Mokhtar A.A., Kamiya T., Serikawa S.

    Proceedings of SPIE - The International Society for Optical Engineering   11884   2021.01

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    In-vehicle cameras and surveillance cameras are used in many situations in our daily lives. Visibility degradation in foggy environments is caused by the scattering of reflected light from real objects by minute water droplets or fog in the medium through which light passes. The degree of degradation depends on the density of suspended microparticles existing between the observed object and the observation point in the medium. In general, the farther the object is from the camera, the more it is affected by the fog. The purpose of image de-fogging is to improve the clarity of an object by removing the effects of fog in the image.

    DOI: 10.1117/12.2603771

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  • A novel single image reflection removal method Reviewed

    Ishiyama S., Lu H., Soomro A.A., Mokhtar A.A.

    Proceedings of SPIE - The International Society for Optical Engineering   11884   2021.01

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    In recent years, reflection is a kind of noise in images which is frequently generated by reflections from windows, glasses and so on when you take pictures or movies. The reflection does not only degrade the image quality, but also affects computer vision tasks such as object detection and segmentation. In SIRR, learning models are often used because various patterns of reflection are possible, and the versatility of the model is required. In this study, we propose a deep learning model for SIRR. There are two problems with the conventional SIRR using deep learning models. The assumed scenes of reflection are vary, and there is little training data because it is difficult to obtain true values. In this study, we focus on the latter and propose an SIRR based on meta-learning. In this study, we adopt MAML, which is one of the methods of meta-learning. In this study, we propose an SIRR using a deep learning model with MAML, which is one of the methods of meta-learning. The deep learning model includes the Iterative Boost Convolutional LSTM Network (IBCLN) is adopted as the deep learning methods. Proposed method improve accuracy compared with conventional method of state-of-the-art result in SIRR.

    DOI: 10.1117/12.2604356

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  • MULTIMODAL TRANSFORMER NETWORKS WITH LATENT INTERACTION FOR AUDIO-VISUAL EVENT LOCALIZATION Reviewed

    He Y., Xu X., Liu X., Ou W., Lu H.

    Proceedings - IEEE International Conference on Multimedia and Expo   2021.01

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    DOI: 10.1109/ICME51207.2021.9428081

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  • LIGHTWEIGHT IMAGE SUPER-RESOLUTION WITH MULTI-SCALE FEATURE INTERACTION NETWORK Reviewed

    Wang Z., Gao G., Li J., Yu Y., Lu H.

    Proceedings - IEEE International Conference on Multimedia and Expo   2021.01

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    DOI: 10.1109/ICME51207.2021.9428136

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  • GRAPH CONVOLUTIONAL HOURGLASS NETWORKS FOR SKELETON-BASED ACTION RECOGNITION Reviewed

    Zhu Y., Xu X., Ji Y., Shen F., Shen H.T., Lu H.

    Proceedings - IEEE International Conference on Multimedia and Expo   2021.01

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    DOI: 10.1109/ICME51207.2021.9428355

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  • Robust Motion Averaging under Maximum Correntropy Criterion Reviewed

    Zhu J., Hu J., Lu H., Chen B., Li Z., Li Y.

    Proceedings - IEEE International Conference on Robotics and Automation   2021-May   5283 - 5288   2021.01

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    DOI: 10.1109/ICRA48506.2021.9561406

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  • Partial feature selection and alignment for multi-source domain adaptation Reviewed

    Fu Y., Zhang M., Xu X., Cao Z., Ma C., Ji Y., Zuo K., Lu H.

    Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition   16649 - 16658   2021.01

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    DOI: 10.1109/CVPR46437.2021.01638

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  • Depth-Distilled Multi-focus Image Fusion Reviewed

    Zhao F., Zhao W., Lu H., Liu Y., Yao L., Liu Y.

    IEEE Transactions on Multimedia   2021.01

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    DOI: 10.1109/TMM.2021.3134565

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  • Action Recognition Framework in Traffic Scene for Autonomous Driving System Reviewed

    Xu F., Xu F., Xie J., Pun C.M., Lu H., Gao H.

    IEEE Transactions on Intelligent Transportation Systems   2021.01

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    DOI: 10.1109/TITS.2021.3135251

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  • Enhancing Audio-Visual Association with Self-Supervised Curriculum Learning Reviewed

    Zhang J., Xu X., Shen F., Lu H., Liu X., Shen H.T.

    35th AAAI Conference on Artificial Intelligence, AAAI 2021   4B   3351 - 3359   2021.01

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  • Characteristics based visual servo for 6DOF robot arm control Reviewed

    Tsuchida S., Lu H., Kamiya T., Serikawa S.

    Cognitive Robotics   1   76 - 82   2021.01

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    DOI: 10.1016/j.cogr.2021.06.002

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  • Temporal Denoising Mask Synthesis Network for Learning Blind Video Temporal Consistency Reviewed

    Zhou Y., Xu X., Shen F., Gao L., Lu H., Shen H.T.

    MM 2020 - Proceedings of the 28th ACM International Conference on Multimedia   475 - 483   2020.10

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    Recently, developing temporally consistent video-based processing techniques has drawn increasing attention due to the defective extend-ability of existing image-based processing algorithms (e.g., filtering, enhancement, colorization, etc). Generally, applying these image-based algorithms independently to each video frame typically leads to temporal flickering due to the global instability of these algorithms. In this paper, we consider enforcing temporal consistency in a video as a temporal denoising problem that removing the flickering effect in given unstable pre-processed frames. Specifically, we propose a novel model termed Temporal Denoising Mask Synthesis Network (TDMS-Net) that jointly predicts the motion mask, soft optical flow and the refining mask to synthesize the temporal consistent frames. The temporal consistency is learned from the original video and the learned temporal features are applied to reprocess the output frames that are agnostic (blind) to specific image-based processing algorithms. Experimental results on two datasets for 16 different applications demonstrate that the proposed TDMS-Net significantly outperforms two state-of-the-art blind temporal consistency approaches.

    DOI: 10.1145/3394171.3413788

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  • Automatic Classification of Respiratory Sounds Based on Time-Frequency Analysis and Convolutional Neural Network Reviewed

    MINAMI Koki, LU Huimin, KIM Hyoungseop, HIRANO Yasushi, MABU Shingo, KIDO Shoji

    Medical Imaging Technology ( The Japanese Society of Medical Imaging Technology )   38 ( 1 )   40 - 47   2020.01

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    <p>Auscultation of respiratory sounds is very important for discovery of respiratory disease. However, there is no quantitative evaluation method for the diagnosis of respiratory sounds. It is necessary to develop a system to support the diagnosis of respiratory sounds. In this paper, we propose an algorithm for the automatic classification of respiratory sounds as normal, continuous sound or crackle. Our approach consists of two major components: 1) transformation of one-dimensional signals into two-dimensional time-frequency representation images using the short-time Fourier transform and the continuous wavelet transform and 2) classification of the images using convolutional neural networks. We applied the proposal method to 22 respiratory sound data. As a result, we achieved the accuracy of 79.44 [%] and the area under the curve based on receiver operating characteristic curve of 0.942.</p>

    DOI: 10.11409/mit.38.40

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  • Recognition of Obstacles from a Spherical Camera Image Based on YOLOv3 Reviewed

    KAI Tomohiro, LU Humin, KAMIYA Tohru

    Proceedings of the Annual Conference of Biomedical Fuzzy Systems Association ( Biomedical Fuzzy Systems Association )   33 ( 0 )   84 - 88   2020.01

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    <p><i>The attention has been focused in using assistive devices at the aging era of Japan. One of the devices is electric wheelchair, which enables physical disability people to easily operate it. However, accidents are occurring frequently with increasing demand by using electric wheelchair. Therefore, the development of an autonomous driving electric wheelchair is required to reduce accidents. In this paper, we propose a recognition of obstacles of panoramic images that obtained from a spherical camera. A spherical camera is equipped in an electric wheelchair, and images are cut out from the sequential images obtained by running. For obstacles recognition, we use YOLOv3. The proposed method considers the distortion of the image caused by using the spherical camera. The improvement of the model of YOLOv3 is examined, and the validity with the actual data is verified. </i></p>

    DOI: 10.24466/pacbfsa.33.0_84

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  • Automatic extraction of abnormalities on temporal ct subtraction images using sparse coding and 3d-cnn Reviewed

    Koizumi Y., Miyake N., Lu H., Kim H., Aoki T., Kido S.

    Proceedings of International Conference on Artificial Life and Robotics   2020   783 - 786   2020.01

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    In recent years, the rate of death from cancer has tended to increase in Japan. Also, there is a concern that increasing the performance of CT will increase the burden on doctors. Therefore, by presenting a "second opinion" in the CAD system, the burden on doctors can be reduced. We develop a CAD system for automatic detection of lung cancer. In this paper we propose a method to detect abnormalities based on temporal subtraction technique, sparse coding and 3D-CNN. We obtain the result that sparse level contributed most to the score.

    DOI: 10.5954/ICAROB.2020.GS3-3

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  • Image Segmentation with Language Referring Expression and Comprehension Reviewed

    Sun J., Li Y., Cai J., Lu H., Serikawa S.

    IEEE Sensors Journal   2020.01

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    Image segmentation with language referring expression can complete object segmentation based on expression text. Existing image segmentation methods show good results on high-performance computers, but most robot systems need real-time and high accuracy. At present, most methods cannot meet these requirements well. Therefore, we propose a high-precision and real-time deep learning network that integrates the two tasks of image segmentation with language referring expression and referring expression comprehension and then treats them as two branches. Specifically, the proposed network first merges the two tasks. The feature maps of different scales extracted by each branch are fed back to the two branches to obtain prediction results. These two tasks promote and restrict each other. Experiments show that our method has better real-time performance and higher accuracy than existing methods.

    DOI: 10.1109/JSEN.2020.3041046

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  • Marine Organisms Tracking and Recognizing Using YOLO Reviewed

    Uemura T., Lu H., Kim H.

    EAI/Springer Innovations in Communication and Computing   53 - 58   2020.01

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    DOI: 10.1007/978-3-030-17763-8_6

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  • Weighted Linear Multiple Kernel Learning for Saliency Detection Reviewed

    Zhou Q., Wu J., Fan Y., Zhang S., Wu X., Zheng B., Jin X., Lu H., Latecki L.J.

    EAI/Springer Innovations in Communication and Computing   201 - 213   2020.01

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    DOI: 10.1007/978-3-030-17763-8_19

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  • Research into the Adaptability Evaluation of the Remote Sensing Image Fusion Method Based on Nearest-Neighbor Diffusion Pan Sharpening Reviewed

    Wang C., Shao W., Lu H., Zhang H., Wang S., Yue H.

    EAI/Springer Innovations in Communication and Computing   33 - 39   2020.01

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    DOI: 10.1007/978-3-030-17763-8_4

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  • Multi-Level Chaotic Maps for 3D Textured Model Encryption Reviewed

    Jin X., Zhu S., Wu L., Zhao G., Li X., Zhou Q., Lu H.

    EAI/Springer Innovations in Communication and Computing   107 - 117   2020.01

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    Language:English   Publishing type:Research paper (international conference proceedings)

    DOI: 10.1007/978-3-030-17763-8_10

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  • A 6-DOF telexistence drone controlled by a head mounted display Reviewed

    Xia X., Pun C.M., Zhang D., Yang Y., Lu H., Gao H., Xu F.

    26th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2019 - Proceedings   1241 - 1242   2019.03

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    DOI: 10.1109/VR.2019.8797791

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  • Introduction to the special section on Artificial Intelligence and Computer Vision Reviewed

    73   378 - 379   2019.01

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    DOI: 10.1016/j.compeleceng.2018.12.003

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  • Classification of Osteoporosis from Phalanges Computed Radiography Images Based on Convolutional Neural Network Reviewed

    HATANO Kazuhiro, MURAKAMI Seiichi, UEMURA Tomoki, LU Humin, KIM Hyoungseop, AOKI Takatoshi

    Medical Imaging and Information Sciences ( MEDICAL IMAGING AND INFORMATION SCIENCES )   36 ( 2 )   72 - 76   2019.01

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    <p>Osteoporosis is known as one of the main diseases of bone. Although image diagnosis for osteoporosis is effective, there are concerns about increased burden of radiologists associated with diagnostic imaging, uneven diagnostic results due to experience difference, and undetected lesions. Therefore, in this study, we propose a diagnosis supporting method for classifying osteoporosis from phalanges computed radiography images and presenting classification results to physicians. In the proposed method, we construct classifiers using convolution neural network and classify normal cases and abnormal cases about osteoporosis. In our experiments, two kinds of CNN models were constructed using input images generated from 101 cases of CR images and evaluated using Area Under the Curve(AUC)value on Receiver Operating Characteristics(ROC)curve. Finaly, AUC of 0.995 was obtained.</p>

    DOI: 10.11318/mii.36.72

    CiNii Article

    Other Link: https://ci.nii.ac.jp/naid/130007668726

  • Automatic Identification of Osteoporosis from Phalanges Computed Radiography Images Based on CNN Reviewed

    HATANO Kazuhiro, MURAKAMI Seiichi, UEMURA Tomoki, LU Huimin, KIM Hyoungseop, AOKI Takatoshi

    Medical Imaging Technology ( The Japanese Society of Medical Imaging Technology )   37 ( 2 )   107 - 115   2019.01

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    <p>Osteoporosis is the main disease of bone. Although image diagnosis for osteoporosis is effective, there are concerns about increased burdens on doctors and variations in diagnostic results due to experience differences of doctors and undetected lesions. Therefore, in this paper, we propose a diagnostic support method to classify osteoporosis from Computed Radiography (CR) images of the phalanges and present classification results to doctors. In the proposed method, we constructed classifiers using Residual Network (ResNet), which is one type of convolution neural network, and classified the presence or absence of osteoporosis. For the input image to ResNet, we used the image generated from CR images. In this paper, we proposed three kinds of input images and conducted training and classification evaluation on each image. In the experiment, the proposed method was applied to 101 cases and evaluated using the Area Under the Curve (AUC) value on the Receiver Operating Characteristics (ROC) curve, the maximum value of which was 0.931.</p>

    DOI: 10.11409/mit.37.107

    CiNii Article

    Other Link: https://ci.nii.ac.jp/naid/130007633096

  • 3D-CNN for Automatic Detection of Lung Nodules from Temporal Subtraction Images Reviewed

    YOSHINO Yuriko, LU Huimin, KIM Hyoungseop, MURAKAMI Seiichi, AOKI Takatoshi, KIDO Shoji

    Medical Imaging and Information Sciences ( MEDICAL IMAGING AND INFORMATION SCIENCES )   36 ( 2 )   77 - 82   2019.01

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    <p>A temporal subtraction image is obtained by subtracting a previous image, which are warped to match between the structures of the previous image and one of a current image, from the current image. The temporal subtraction technique removes normal structures and enhances interval changes such as new lesions and changes of existing abnormalities from a medical image. However, many artifacts remain on a temporal subtraction image and these can be detected as false positives on the subtraction images. In this paper, we propose a 3D-CNN after initial nodule candidates are detected using temporal subtraction technique. To compare the proposed 3D-CNN, we used 7 model architectures, which are 3D ShallowNet, 3D-AlexNet, 3D-VGG11, 3D-VGG13, 3D-ResNet8, 3D-ResNet20, 3D-ResNet32, with these performance on 28 thoracic MDCT cases including 28 small-sized lung nodules. The higher performance is showed on 3D-AlexNet.</p>

    DOI: 10.11318/mii.36.77

    CiNii Article

    Other Link: https://ci.nii.ac.jp/naid/130007668727

  • Swallowing motion analyzing from dental MR imaging based on AKAZE and particle filter algorithm Reviewed

    2018-October   1343 - 1346   2018.12

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  • ROI-based fully automated liver registration in multi-phase CT images Reviewed

    2018-October   645 - 649   2018.12

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  • Extraction of median plane from facial 3D point cloud based on symmetry analysis using ICP algorithm Reviewed

    2018-October   1347 - 1350   2018.12

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  • Enhancement of bone metastasis from CT images based on salient region feature registration Reviewed

    2018-October   1329 - 1332   2018.12

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  • Detection of phalange region based on U-Net Reviewed

    2018-October   1338 - 1342   2018.12

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  • Detection of grasping position from video images based on SSD Reviewed

    2018-October   1472 - 1475   2018.12

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  • Detection of abnormal shadows on temporal subtraction images based on multi-phase CNN Reviewed

    2018-October   1333 - 1337   2018.12

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  • Segmentation of Spinal Canal Region in CT Images using 3D Region Growing Technique Reviewed

    2018.11

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    DOI: 10.1109/ICT-ROBOT.2018.8549913

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  • Object Detection on Video Images Based on R-FCN and GrowCut Algorithm Reviewed

    2018.11

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    DOI: 10.1109/ICT-ROBOT.2018.8549879

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  • Dual Learning for Visual Question Generation Reviewed

    2018-July   2018.10

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    DOI: 10.1109/ICME.2018.8486475

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  • Statistical shape model generation using K-means clustering Reviewed

    207 - 211   2018.09

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    DOI: 10.1145/3277453.3277467

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  • FDCNet: filtering deep convolutional network for marine organism classification Reviewed

    77 ( 17 )   21847 - 21860   2018.09

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    Language:English   Publishing type:Research paper (scientific journal)

    DOI: 10.1007/s11042-017-4585-1

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  • Editorial: Intelligent Industrial IoT Integration with Cognitive Computing Reviewed

    23 ( 2 )   185 - 187   2018.04

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    Language:English   Publishing type:Research paper (scientific journal)

    DOI: 10.1007/s11036-017-0939-1

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  • Editorial: Artificial Intelligence for Mobile Robotic Networks Reviewed

    23 ( 2 )   326 - 327   2018.04

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    DOI: 10.1007/s11036-017-0938-2

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  • Proposal of a power-saving unmanned aerial vehicle Reviewed

    2017-September   2018.01

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    DOI: 10.4108/eai.28-9-2017.2273334

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  • Object Detection on Video Images Based on R-FCN and GrowCut Algorithm Reviewed

    Mouri Kousuke, Lu Huimin, Kim Hyoungseop

    Proceedings of IIAE Annual Conference ( The Institute of Industrial Applications Engineers )   2018 ( 0 )   85 - 86   2018.01

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    DOI: 10.12792/iiae2018.044

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    Other Link: https://ci.nii.ac.jp/naid/130007542637

  • Automatic Identification of Osteoporosis from Phalanges Computed Radiography Images Based on Deep Convolutional Neural Network Reviewed

    HATANO Kazuhiro, MURAKAMI Seiichi, UEMURA Tomoki, LU Huimin, TAN Joo Kooi, KIM Hyoungseop, AOKI Takatoshi

    Medical Imaging Technology ( The Japanese Society of Medical Imaging Technology )   36 ( 2 )   90 - 95   2018.01

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    Osteoporosis is known as a disease of bone. Visual screening using Computed Radiography (CR) images is an effective method for osteoporosis; however, there are many diseases that exhibit similar state of low bone mass. In this paper, we propose an automatic identification method of osteoporosis from phalanges CR images. As the proposed method, we implement a classifier based on Deep Convolutional Neural Network (DCNN) to identify unknown CR images as normal or abnormal. For training and evaluating of DCNN, we use pseudo color images. The pseudo color images are generated by assigning three types of ROI to R, G, and B channels after extracting the ROI from inside the phalange region of the three kinds of images created from the CR image. In the experiment, we apply our proposal method to 101 cases and True Positive Rate of 75.5 [%] and False Positive Rate of 13.9 [%] were obtained.

    DOI: 10.11409/mit.36.90

    CiNii Article

    Other Link: https://ci.nii.ac.jp/naid/130006588793

  • Introduction to the special section on Artificial Intelligence and Computer Vision

    Lu H., Guna J., Dansereau D.

    Computers and Electrical Engineering ( Computers and Electrical Engineering )   58   444 - 446   2017.02

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    DOI: 10.1016/j.compeleceng.2017.04.024

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  • Transfer-learning deep convolutional neural network for classification of polyp candidates on CT colonography Reviewed

    UEMURA Tomoki, LU Huimin, KIM Hyoungseop, TACHIBANA Rie, HIRONAKA Toru, Janne J. Näppi, YOSHIDA Hiroyuki

    Medical Imaging and Information Sciences ( MEDICAL IMAGING AND INFORMATION SCIENCES )   34 ( 2 )   80 - 86   2017.01

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    <p>Computed tomographic colonography(CTC), also known as virtual colonoscopy, provides a minimally invasive screening method for early detection of colorectal lesions. It can be used to solve the problems of accuracy, capacity, cost,and safety that have been associated with conventional colorectal screening methods. Computer-aided detection(CADe)has been shown to increase radiologists' sensitivity and to reduce inter-observer variance in detecting colonic polyps in CTC. However, although CADe systems can prompt locations of abnormalities at a higher sensitivity than that of radiologists,they also prompt relatively large numbers of false positives(FPs). In this study, we developed and evaluated the effect of a transfer-learning deep convolutional neural network(TL-DCNN)on the classification of polyp candidates detected by a CADe system from dual-energy CTC images. A deep convolution neural network(DCNN)that had been pre-trained with millions of natural non-medical images was fine-tuned to identify polyps by use of pseudo-colored images that were generated by assigning axial, coronal, and sagittal images of the polyp candidates to the red, green, and blue channels of the images, respectively. The classification performances of the TL-DCNN and the corresponding non-transfer-learning DCNN were evaluated by use of 5-fold cross validation on 20 clinical CTC cases. The TL-DCNN yielded true- and falsepositive rates of 73.6[%]and 1.79[%], respectively, which were significantly higher than those of the non-transferlearning DCNN. This preliminary result demonstrates the effectiveness of the TL-DCNN in the classification of polyp candidates from CTC images.</p>

    DOI: 10.11318/mii.34.80

    CiNii Article

    Other Link: https://ci.nii.ac.jp/naid/130006846731

  • Automatic identification of circulating tumor cells in fluorescence microscopy images based on logical conjunction of cell regions Reviewed

    TSUJI Kouki, LU Huimin, TAN Joo Kooi, KIM Hyoungseop, YONEDA Kazue, TANAKA Fumihiro

    Medical Imaging and Information Sciences ( MEDICAL IMAGING AND INFORMATION SCIENCES )   34 ( 4 )   151 - 155   2017.01

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    <p>Circulating tumor cells(CTCs)can be a useful biomarker. They may have some information about the malignant disease, since they are one of causes of the cancer metastasis. The blood sample from cancer patient is analyzed by fluorescence microscope. This microscope takes enlarged images with three types of lights(red, green and blue),and specific materials are reacted respectively. The blood contains a lot of cells, but there are few CTCs. Therefore analyzing them is not easy work for pathologists. In this study, we develop a method which detects circulating tumor cells in fluorescence microscopy images automatically. Our proposed method has three steps. First, we extract cell regions in microscopy images by using filtering processing. Second, we separate the connecting cell regions into single cell regions,based on the branch and bound algorithm. Finally, we identify CTCs by using logical conjunction method. We demonstrated the effectiveness of our proposed method using 6 cases(5040 microscopy images), and we evaluated the performance of CTCs identification. Our proposed method achieved, a true positive rate of 95.27 [%] and a false positive rate of 6.172 [%] respectively. And we confirmed the effectiveness of the logical conjinction for CTCs identicication.</p>

    DOI: 10.11318/mii.34.151

    CiNii Article

    Other Link: https://ci.nii.ac.jp/naid/130006267703

  • Development of Speed Control System by Intelligent Sensing of Droning Motor Abnormality Reviewed

    Kihara Keita, Lu Huimin, Yang Shiyuan, Serikawa Seiichi

    Proceedings of IIAE Annual Conference ( The Institute of Industrial Applications Engineers )   2017 ( 0 )   33 - 34   2017.01

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    DOI: 10.12792/iiae2017.018

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    Other Link: https://ci.nii.ac.jp/naid/130006688858

  • Detection of Ground Glass Opacity Regions on LIDC Datasets Using DCNN Reviewed

    HIRAYAMA Kazuki, LU Huimin, TAN Joo Kooi, KIM Hyoungseop, TACHIBANA Rie, HIRANO Yasushi, KIDO Shoji

    Medical Imaging and Information Sciences ( MEDICAL IMAGING AND INFORMATION SCIENCES )   34 ( 2 )   70 - 74   2017.01

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    <p>Lung cancer is one of the most important cancer in the world. Among them, Ground Glass Opacity(GGO)has a hazy area of increased attenuation in the lung image. In recent years, development of a Computer Aided Diagnosis (CAD)system for reducing the burden on work load and improving the detection rate of lesions has been advanced. In this paper, we propose a CAD system to extract GGO from CT images. Firstly, we extract the lung region from the input CT images and remove the vessel, and bronchial region based on 3 D line filter algorithm. After that, we extract initial GGO regions using concentration and gradient information. Next, we calculate the statistical features on the segmented regions. After that, we classify GGO regions using support vector machine(SVM). Finally, we detect the final GGO regions using deep convolutional neural network(DCNN). The proposed method is tested on 31 cases of CT images from the Lung Image Database Consortium(LIDC). The results demonstrate that the proposed method has 86.05[%] of true positive rate and 39.03[/case] of false positive number.</p>

    DOI: 10.11318/mii.34.70

    CiNii Article

    Other Link: https://ci.nii.ac.jp/naid/130006846732

  • Super-resolving the Depth Map for 3D Deep-sea Terrain Reconstruction Reviewed

    Lu Huimin, Kashio Yohei, Koga Yosuke, Li Yujie, Nakashima Shota, Zhang Lifeng, Jože Guna, Serikawa Seiichi

    Proceedings of IIAE Annual Conference ( The Institute of Industrial Applications Engineers )   2015 ( 0 )   60 - 61   2015.01

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    DOI: 10.12792/iiae2015.031

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  • Enhancing underwater image by dehazing and colorization Reviewed

    7 ( 7 )   3470 - 3474   2012.12

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  • A novel safety light curtain system using a hemispherical mirror Reviewed

    8561   2012.12

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    DOI: 10.1117/12.999722

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▼display all

Publications (Books)

  • Cognitive Internet of Things: Frameworks, Tools and Applications

    (Sole author)

    2020.01  ( ISBN:978-3-030-04945-4

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    Language:English

  • Artificial Inteligence and Robotics

    Huimin Lu, Xing Xu(Joint editor)

    Springer  2017.12  ( ISBN:978-3-319-69877-9

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    Language:Japanese

  • Artificial Intelligence and Computer Vision

    Huimin Lu, Yujie Li(Joint editor)

    Springer  2016.11 

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    Language:English

Lectures

  • AIを活用した水中画像処理技術と深海資源調査への展開

    第1回海中海底工学フォーラム・ZERO  2019.04 

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    Presentation type:Invited lecture  

  • Artificial Intelligence in Deep-sea Observing

    The 2nd International Symposium on Artificial Intelligence and Robotics 2017  2017.11  ISAIR

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    Presentation type:Invited lecture  

  • Extreme Optical Imaging for Deep-sea Observing Network

    26th International Electrotechnical and Computer Science Conference ERK 2017  2017.09  IEEE Slovenia

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    Presentation type:Invited lecture  

  • Next Generation Artificial Intelligence in Society 5.0

    IBM Australia Seminar  2017.09  IBM Australia

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    Presentation type:Discussion meeting  

Grants-in-Aid for Scientific Research

  • 日中超スマート社会の実現に向けた次世代のAI/IoTに関する研究

    Grant number:00000001  2018.04 - 2019.03   二国間国際交流事業

  • 深海採鉱機採削時の画像計測システムの研究開発

    Grant number:17K14694  2017.04 - 2019.03   若手研究(B)

  • 深海採鉱機向けリアルタイム小型イメージングシステムの研究開発

    Grant number:15F15077  2015.04 - 2016.09   特別研究員奨励費

  • 深海採鉱機向け鉱床計測用リアルタイム画像採取処理装置の研究開発

    Grant number:13J10713  2013.04 - 2015.03   特別研究員奨励費

Contracts

  • 国立情報学研究所共同研究

    2018.04 - 2019.03

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    Grant type:Consigned research

  • 国立研究開発法人情報通信研究機構国際交流プログラム

    2018.04 - 2019.03

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    Grant type:Consigned research

Activities of Academic societies and Committees

  • IEEE Computer Society Big Data Special Technical Committee   Co-Chair  

    2019.08