Hongfeng Li - IEEE Xplore Author Profile
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Hongfeng Li

Publications
4
Citations
37
Publications by Year
20142022
Co-Authors:
Show All Co-Authors (12)
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Hongfeng Li

Affiliation

Institute of Medical Technology, Peking University Health Science Center, China

Publication Topics

Biography

Hongfeng Li received the B.S. degree in mathematics and applied mathematics and the Ph.D. degree in computer software and theory from the Huazhong University of Science and Technology, Wuhan, China, in 2010 and 2016, respectively. From 2016 to 2018, he was a Postdoc with Beijing International Center for Mathematical Research, Peking University, Beijing, China. He is currently a Research Scientist with the Institute of Medical Technology, Peking University Health Science Center, Beijing, China. His research interests include machine or deep learning, optimization, and their applications in image processing.(Based on document published on 25 January 2022).
Publications
4
Citations
37
Publications by Year
20142022
Co-Authors:
Show All Co-Authors (12)
Author's Published Works

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Feature Fusion for Segmentation and Classification of Skin Lesions

Yue Zhang;Zifan Chen;Hao Yu;Xinyu Yao;Hongfeng Li

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)
Year: 2022 | Conference Paper |
Cited by: Papers (3)
Automated segmentation and classification of dermoscopy images are two crucial tasks for early detection of skin cancers. Deep models trained for individual task ignore the relationship of the two tasks and lack the diagnostic proposals or explanation for diagnosis results in practice. We assume that features extracted with segmentation models and classification models on the same dataset are high...Show More

Feature Fusion for Segmentation and Classification of Skin Lesions

Yue Zhang;Zifan Chen;Hao Yu;Xinyu Yao;Hongfeng Li

Year: 2022 | Conference Paper |

Hyperspectral Image Classification Using Adaptive Weighted Quaternion Zernike Moments

Huizhen Li;Hua Huang;Zhijing Ye;Hongfeng Li

IEEE Transactions on Signal Processing
Year: 2022 | Volume: 70 | Journal Article |
Cited by: Papers (9)
Hyperspectral image classification (HSI) has been widely used in many fields. However, image noise, atmospheric conditions, material distribution and other factors seriously degrade the classification accuracy of HSIs. To alleviate these issues, a new approach, namely adaptive weighted quaternion Zernike moments (AWQZM), is proposed, which extracts effective spatial-spectral features for pixels in...Show More

Hyperspectral Image Classification Using Adaptive Weighted Quaternion Zernike Moments

Huizhen Li;Hua Huang;Zhijing Ye;Hongfeng Li

Year: 2022 | Volume: 70 | Journal Article |

Neural-Response-Based Extreme Learning Machine for Image Classification

Hongfeng Li;Hongkai Zhao;Hong Li

IEEE Transactions on Neural Networks and Learning Systems
Year: 2019 | Volume: 30, Issue: 2 | Journal Article |
Cited by: Papers (24)
This paper proposes a novel and simple multilayer feature learning method for image classification by employing the extreme learning machine (ELM). The proposed algorithm is composed of two stages: the multilayer ELM (ML-ELM) feature mapping stage and the ELM learning stage. The ML-ELM feature mapping stage is recursively built by alternating between feature map construction and maximum pooling op...Show More

Neural-Response-Based Extreme Learning Machine for Image Classification

Hongfeng Li;Hongkai Zhao;Hong Li

Year: 2019 | Volume: 30, Issue: 2 | Journal Article |

Relaxed collaborative representation for face recognition based low-rank matrix recovery

Rokan Khaji;Hong Li;Taha Mohammed Hasan;Hongfeng Li;Qabas Ali

2014 International Conference on Wavelet Analysis and Pattern Recognition
Year: 2014 | Conference Paper |
Cited by: Papers (1)
Face recognition is of paramount importance in computer vision and biometrics systems. In this paper we propose an improved method which is suitable to handle variations in image configurations like pose, illumination, and facial expressions as well as occlusion and disguise, in order to provide high efficiencyi in the face recognition. This method integrates the low-rank matrix which is recovered...Show More

Relaxed collaborative representation for face recognition based low-rank matrix recovery

Rokan Khaji;Hong Li;Taha Mohammed Hasan;Hongfeng Li;Qabas Ali

Year: 2014 | Conference Paper |

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