LU Huimin



Associate Professor


1-1 Sensui-cho, Tobata-ku, Kitakyushu-shi, Fukuoka

Research Fields, Keywords

Artificial Intelligence, Robotics, Oceanic Optics, Computer Vision




Degree 【 display / non-display

  • Kyushu Institute of Technology -  Doctor (Engineering)  2014.03

Biography in Kyutech 【 display / non-display

  • 2019.09

    Kyushu Institute of TechnologyFaculty of Engineering   Department of Mechanical and Control Engineering   Associate Professor  

Specialized Field (scientific research fund) 【 display / non-display

  • Earth system and resources engineering

  • Perceptual information processing

  • Measurement engineering


Publications (Article) 【 display / non-display

  • DeepEye: A Dedicated Camera for Deep-Sea Tripod Observation Systems

      810   507 - 511   2020.01  [Refereed]

     View Summary

    © 2020, Springer Nature Switzerland AG. The deep-sea tripod systems are designed and built at the U.S. Geological Survey (USGS) Pacific Coastal and Marine Science Center (PCMSC) in Santa Cruz, California. They are recovered in late September 2014 after spending about half a year collecting data on the floor of the South China Sea. The deep-sea tripod systems are named as Free-Ascending Tripod (FAT), are deployed at 2,100 m water depth—roughly 10 times as deep as most tripods dedicated to measuring currents and sediment movement at the seafloor. Deployment at this unusual depth was made possible by the tripod’s ability to rise by itself to the surface rather than being pulled up by a line. Instruments mounted on the tripod took bottom photographs and measured such variables as water temperature, current velocity, and suspended-sediment concentration. FAT is used to better understand how and where deep-seafloor sediment moves and accumulates. Besides of this, we also use them to study the deep-sea biology. The obtained the images from the camera, the biology animals are hardly to be distinguished. In this project, we are concerned to use novel underwater imaging technologies for recovering the deep-sea scene.

    DOI Scopus

  • Saliency Detection via Objectness Transferring

      810   201 - 211   2020.01  [Refereed]

     View Summary

    © 2020, Springer Nature Switzerland AG. In this paper, we present a novel framework to incorporate top-down guidance to identify salient objects. The salient regions/objects are predicted by transferring objectness prior without the requirement of center-biased assumption. The proposed framework consists of the following two basic steps: In the top-down process, we create a location saliency map (LSM), which can be identified by a set of overlapping windows likely to cover salient objects. The corresponding binary segmentation masks of training windows are treated as high-level knowledge to be transferred to the test image windows, which may share visual similarity with training windows. In the bottom-up process, a multi-layer segmentation framework is employed, providing local shape information that is used to delineate accurate object boundaries. Through integrating top-down objectness priors and bottom-up image representation, our approach is able to produce an accurate pixel-wise saliency map. Extensive experiments show that our approach achieves the state-of-the-art results over MSRA 1000 dataset.

    DOI Scopus

  • Nuclear Norm Regularized Structural Orthogonal Procrustes Regression for Face Hallucination with Pose

      810   159 - 169   2020.01  [Refereed]

     View Summary

    © 2020, Springer Nature Switzerland AG. In real applications, the observed low-resolution (LR) face images usually have pose variations. Conventional learning based methods ignore these variations, thus the learned representations are not beneficial for the following reconstruction. In this paper, we propose a nuclear norm regularized structural orthogonal Procrustes regression (N2SOPR) method to learn pose-robust feature representations for efficient face hallucination. The orthogonal Procrustes regression (OPR) seeks an optimal transformation between two images to correct the pose from one to the other. Additionally, our N2SOPR uses the nuclear norm constraint on the error term to keep image’s structural information. A low-rank constraint on the representation coefficients is imposed to adaptively select the training samples that belong to the same subspace as the inputs. Moreover, a locality constraint is also enforced to preserve the locality and the sparsity simultaneously. Experimental results on standard face hallucination databases indicate that our proposed method can produce more reasonable near frontal face images for recognition purpose.

    DOI Scopus

  • Hyperspectral Images Segmentation Using Active Contour Model for Underwater Mineral Detection

      810   513 - 522   2020.01  [Refereed]

     View Summary

    © 2020, Springer Nature Switzerland AG. In this paper, we design a novel underwater hyperspectral imaging technique for deep-sea mining detection. The spectral sensitivity peaks are in the region of the visible spectrum, 580, 650, 720, 800 nm. In addition, to the underwater objects recognition, because of the physical properties of the medium, the captured images are distorted seriously by scattering, absorption and noise effect. Scattering is caused by large suspended particles, such as in turbid water, which contains abundant particles, algae, and dissolved organic compounds. In order to resolve these problems of recognizing mineral accurately, fast and effectively, an identifying and classifying algorithm is proposed in this paper. We take the following steps: firstly, through image preprocessing, hyperspectral images are gained by using denoising, smoothness, image erosion. After that, we segment the cells by the method of the modified active contour method. These methods are designed for real-time execution on limited-memory platforms, and are suitable for detecting underwater objects in practice. The Initial results are presented and experiments demonstrate the effectiveness of the proposed imaging system.

    DOI Scopus

  • Preface

    Lu H.

    Studies in Computational Intelligence    810   2020.01


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Publications (Books) 【 display / non-display

  • Cognitive Internet of Things: Frameworks, Tools and Applications

    ( Single Work )

    2020.01 ISBN: 978-3-030-04945-4

  • Artificial Inteligence and Robotics

    Huimin Lu, Xing Xu ( Joint Editor )

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

  • Artificial Intelligence and Computer Vision

    Huimin Lu, Yujie Li ( Joint Editor )

    Springer  2016.11

Lectures 【 display / non-display

  • 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   2019.04.12 

  • Artificial Intelligence in Deep-sea Observing

    The 2nd International Symposium on Artificial Intelligence and Robotics 2017   2017.11.25  ISAIR

  • Extreme Optical Imaging for Deep-sea Observing Network

    26th International Electrotechnical and Computer Science Conference ERK 2017   2017.09.25  IEEE Slovenia

  • Next Generation Artificial Intelligence in Society 5.0

    IBM Australia Seminar   2017.09.04  IBM Australia


Activities of Academic societies and Committees 【 display / non-display

  • 2019.08

    IEEE Computer Society Big Data Special Technical Committee   Co-Chair