TS. Đỗ Như Tài

Amanda Wilson

Đỗ Như Tài

Giảng viên

Tiến sĩ

Chonnam National University, Korea

taidn@ueh.edu.vn
Orcid Scholar Researchgate
Lĩnh vực nghiên cứu:Computer visionDeep LearningEmotion RecognitionMedical Image AnalysisObject TrackingPattern Recognition

STUDY

2005: Bachelor of Information Technology, Ho Chi Minh City University of Foreign Languages – Information Technology
2016: Master of Engineering in Information Technology Management, International University, Vietnam National University, Ho Chi Minh City
2017 – Current: Department of Artificial Intelligent Convergence, Chonnam National University, South Korea

PUBLICATION

  1. Nhu-Tai Do, Sung-Taek Jung, Hyung-Jeong Yang, Soo-Hyung Kim, 2021, “Multi-Level Seg-Unet Model with Global and Patch-Based X-Ray Images,” Diagnostics, 2020
  2. Nhu-Tai Do, Soo-Hyung Kim, Hyung-Jeong Yang, Guee-Sang Lee, Soonja Yeom, 2021, “Contextaware Emotion Recognition in the wild using Spatio-temporal and Temporal-pyramid Models,” Sensors, 21, 2344, pp. 1-29, March 2021. (IF: 3.275) (BRL2/지역우수)
  3. Tram-Tran Nguyen-Quynh, Soo-Hyung Kim, Nhu-Tai Do, 2020, “Image Colorization Using the Global Scene-Context Style and Pixel-Wise Semantic Segmentation,” IEEE Access, Vol. 8, pp. 214098-214114, Nov. 2020. (IF: 3.745, NRF2/BRL2)
  4. Nhu-Tai Do, Soo-Hyung Kim, Hyung-Jeong Yang, and Guee-Sang Lee, 2020, “Robust Hand Shape Features for Dynamic Hand Gesture Recognition using Multi-Level Feature LSTM,” Applied Science, 10(18), 6293, pp. 1-28, doi:10.3390/app10186293, Sep. 2020. (IF: 2.474) (NRF2/BRL2)
  5. Do Nhu Tai, In Seop Na, Soo Hyung Kim, 2019, “HSFE Network and Fusion Model based Dynamic Hand Gesture Recognition,” KSII Trans. Internet and Information Systems, Vol.14, No. 9, pp. 3924-3940, Sep. 2019. (IF 0.648, BRL)
  6. Son Thai Ly, Nhu-Tai Do, Soo-Hyung Kim, Hyung-Jeong Yang, Guee-Sang Lee, 2019, “A novel 2D and 3D multimodal approach for in-the-wild facial expression recognition,” Image and Vision Computing, 92, 103817, pp. 1-12, DOI: 10.1016/j.imavis.2019.10.003, Dec. 2019. (IF: 3.103, BRL)
  7. Nhu-Tai Do, Sung-Taek Jung, Soo-Hyung Kim, 2021, “Boosting up knee bone tumor detection from radiology and magnetic resonance imaging by using deep learning techniques,” Proc. AACR Virtual Special Conf. Artificial Intelligence, Diagnosis, and Imaging, Jan. 2021.
  8. Nhu-Tai Do, Tram-Tran Nguyen-Quynh, Soo-Hyung Kim, 2020, “Affective Expression Analysis inthe-Wild using Multi-Task Temporal Statistical Deep Learning Model,” Proc. 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), doi:
  9. 10.1109/FG47880.2020.00093, pp. 624-628, Buenos Aires, Argentina, Nov. 2020. (BRL/지역우수)
  10. Tram-Tran Nguyen-Quynh, Nhu-Tai Do, Soo-Hyung Kim, 2020, “MLEU: Multi-Level Embedding U-Net for Fully Automatic Image Colorization,” Proc. Int. Conf. Machine Learning and SmartComputing (ICMLSC 2020), pp. 119-123, Haiphong City, Vietnam, Jan. 2020. (BRL) (Best Presentation Award)
  11. Son Thai Ly, Nhu-Tai Do, Guee-Sang Lee, Soo-Hyung Kim, Hyung-Jeong Yang, 2019, “A 3D Face Modeling Approach for In-the-wild Facial Expression Recognition on Image Datasets,” Proc. 2019 IEEE Int. Conf. Image Processing (ICIP 2019), pp. 3492-3496, Taipei, Taiwan, Sep. 2019. (BRL)
  12. Son Thai Ly Nhu-Tai Do, Guee-Sang Lee, Soo-Hyung Kim, Hyung-Jeong Yang, 2019, “Multimodal 2D and 3D for In-the-wild Facial Expression Recognition,” Proc. 2019 IEEE CVPR Precognition Workshop, Long Beach, USA, June 2019. (BRL)
  13. Nhu-Tai Do, Sang-Don Joo, Hyung-Jeong Yang, Sung Taek Jung and Soo-Hyung Kim, 2019, “Knee Bone Tumor Segmentation from radiographs using Seg-Unet with Dice Loss,” Proc. Int. Workshop on Frontiers in Computer Vision (IWFCV 2019), Gangnung, Korea, Feb. 2019. (LINC+)
  14. Nhu-Tai Do, In-Seop Na, Hyung-Jeong Yang, Guee-Sang Lee, Soo-Hyung Kim, 2018, “Forensics Face Detection from GANs Using Convolutional Neural Network,” Proc. 2018 Int. Symp. Information Technology Convergence, pp. 376-379, Jeonju, Korea, Oct. 2018. (BRL/GCS)
  15. Nhu-Tai Do, In-Seop Na and Soo-Hyung Kim, 2018, “Face Tracking with Convolutional Neural Network Heat-Map,” Proc. 2nd Int. Conf. Machine Learning and Soft Computing, pp. 29-33, Phu Quoc, Vietnam, Feb. 2018. (BRL/GCS)
  16. 2020 도누따이, 정성택, 양형정, 김수형, “다단계 Seg-Unet 모델을 이용한 방사선 사진에서의 End-to-end 골 종양 분할 및 분류,” 정보과학회논문지, 제 47 권 제 2 호, pp. 170-179, 2020 년 2 월. (의사과학자)
    Nhu-Tai Do, Sung-Taek Jung, Hyung-Jeong Yang, Soo-Hyung Kim, “End-to-end Bone Tumor Segmentation and Classification from X-ray Images by Using Multi-level Seg-Unet Model”, 정보과학회논문지, pp. 170-179, 2020 년 2 월
  17. Do Nhu Tai, Soo-Hyung Kim, Guee-Sang Lee, Hyung-Jeong Yang, In-Seop Na, A-Ran Oh, 2018, “Tracking by Detection of Multiple Faces using SSD and CNN Features,”스마트미디어저널, 7 권 4 호, pp. 61-69, 2018 년 12 월. (BRL/지역우수)
  18. Tram-Tran Nguyen Quynh, Nhu-Tai Do, 2020, 오아란, 김수형, “Generative Adversarial Model 을이용한 자연경관 이미지의 컨텍스트 색채화”, 2020 한국스마트미디어학회 추계학술대회 발표논문집, Vol. 9, No. 2, pp. 86-89, 광주시청자미디어센터, 2020 년 11 월. (지역우수/BRL2)
  19. Nguyen-Quynh Tram-Tran, 김수형, Do Nhu-Tai, 2020, “U-net 을 사용한 자연 경관 흑백 이미지의 색채화”, 2020 한국스마트미디어학회 춘계학술대회 발표논문집, Vol. 9, No. 1, pp. 84-88, 조선대학교, 2020 년 5 월. (지역우수) (Colorization of Natural Scene Image using U-net)
  20. Nhu-Tai Do, Tram-Tran Nguyen-Quynh, Thanh-Hung Vo, 정성택, 김수형, 2019, “Seg-Unet 및 Dice Loss 를 이용한 방사선 사진에서의 End-to end 뼈 종양 분할,” 2019 년도 한국컴퓨터종합학술대회 논문집(KCC 2019), pp. 799-801, 제주 ICC, 2019 년 6 월. (BRL/LINC+)(★우수논문)
    (End-to-End Knee Bone Tumor Segmentation and Classification from Radiographs using SegUnet and Dice Loss)
  21. Nhu-Tai Do, Son Thai Ly, Guee-Sang Lee, Jung Hee Kim, Soo-Hyung Kim, 2019, “A New Approach to Facial Emotion Recognition in-the-wild using 3DFace Reconstruction,” 한국스마트미디어학회 2019 년도 춘계학술대회 학술발표논문집, 제 8 권, 제 1 호, pp. 441-442, 한국교통대학교, 2019 년 4 월. (BRL)
  22. Nhu-Tai Do, Hai-Duong Nguyen, Young Chul Kim, Guee-Sang Lee, Hyung Jeong Yang, SooHyung Kim, 2018, “Depth-based Hand Interaction System using Convolution Neural Network,”한국스마트미디어학회 2018 년도 춘계학술대회 학술발표논문집, 제 7 권, 제 1 호, pp. 98-100, 중앙대학교, 2018 년 4 월. (GCS/BRL)
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