Research output per year
Research output per year
Personal profile
In Korean
김정홍 교수(IT대학 컴퓨터학부)
Education
o (2012) B.A., Kyungpook National University
o (2015) M.A., Kyungpook National University
o (2020) Ph.D., Chungnam National University
o (2015) M.A., Kyungpook National University
o (2020) Ph.D., Chungnam National University
Professional Experience
o (1988 ~ 1996) Senior Researcher, ETRI
o (1996 ~ 2008) Sangju National University
o (2008 ~ present) Kyungpook National University
o (1996 ~ 2008) Sangju National University
o (2008 ~ present) Kyungpook National University
Research Interests
Multimedia Information Processing, Computer vision using deep learning methods, Anomaly detection in image and video, Self-supervised learning image classification and visualization. Medical Image Segmentation.
Major Research Achievements
o Extensive knowledge distillation model: an end-to-end effective anomaly detection model for real-time industrial applications
o Consecutive multiscale feature learning-based image classification model
o Digital Twin Model: A Real-Time Emotion Recognition System for Personalized Healthcare
o An integrated mediapipe-optimized GRU model for Indian sign language recognition
o UzADL: Anomaly detection and localization using graph Laplacian matrix-based unsupervised learning method
o REF-Net: Robust, Efficient, and Fast Network for Semantic Segmentation Applications Using Devices With Limited Computational Resources
o FU-Net: Fast Biomedical Image Segmentation Model based on Bottleneck Convolution Layers
o Consecutive multiscale feature learning-based image classification model
o Digital Twin Model: A Real-Time Emotion Recognition System for Personalized Healthcare
o An integrated mediapipe-optimized GRU model for Indian sign language recognition
o UzADL: Anomaly detection and localization using graph Laplacian matrix-based unsupervised learning method
o REF-Net: Robust, Efficient, and Fast Network for Semantic Segmentation Applications Using Devices With Limited Computational Resources
o FU-Net: Fast Biomedical Image Segmentation Model based on Bottleneck Convolution Layers
url
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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Airy YOLOv5 for Disabled Sign Detection
Rakhmonov, A. A. U., Subramanian, B., Kim, T. & Kim, J., 2023, ICUFN 2023 - 14th International Conference on Ubiquitous and Future Networks. IEEE Computer Society, p. 869-874 6 p. (International Conference on Ubiquitous and Future Networks, ICUFN; vol. 2023-July).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
1 Scopus citations -
Consecutive multiscale feature learning-based image classification model
Olimov, B., Subramanian, B., Ugli, R. A. A., Kim, J. S. & Kim, J., Dec 2023, In: Scientific Reports. 13, 1, 3595.Research output: Contribution to journal › Article › peer-review
Open Access14 Scopus citations -
Electronics Engineering Perspectives on Computer Vision Applications: An Overview of Techniques, Sub-areas, Advancements and Future Challenges
Zheng, Y. X., Chee, K. W. G. H. A., Paul, A., Kim, J. & Lv, H., 2023, Studies in Computational Intelligence. Springer Science and Business Media Deutschland GmbH, p. 113-142 30 p. (Studies in Computational Intelligence; vol. 1118).Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review
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Extensive Knowledge Distillation Model: An End-to-End Effective Anomaly Detection Model for Real-Time Industrial Applications
Rakhmonov, A. A. U., Subramanian, B., Olimov, B. & Kim, J., 2023, In: IEEE Access. 11, p. 69750-69761 12 p.Research output: Contribution to journal › Article › peer-review
Open Access6 Scopus citations -
Retraction Note: Energy efficient topology management scheme based on clustering technique for software defined wireless sensor network (Peer-to-Peer Networking and Applications, (2019), 12, 2, (348-356), 10.1007/s12083-017-0607-z)
Din, S., Paul, A., Ahmad, A. & Kim, J. H., May 2023, In: Peer-to-Peer Networking and Applications. 16, 3, p. 1581 1 p.Research output: Contribution to journal › Comment/debate
Open Access