Đỗ Thanh Hà

Đỗ Thanh Hà, Doctor
Personal page
Function:
Associate Dean of Faculty
Office:
T5-505
VNU mail:
hadt_tct@vnu.edu.vn
Research Fields:
Pattern Recognition and Document Image Analysis, Medical Image Processing , Optimize the computer vision algorithms in autonomous car , Machine Learning , Computer Vision
Education :
  • Doctor, 2010, Informatique, Université de Lorraine, France
  • Master, 2007, Mathematics, VNU Hanoi University of Science
  • Bachelor, 2005, Applied Mathematics and Informatics, VNU Hanoi University of Science
Teaching:
  • Image Processing
  • Programming in C/C++, Java
  • Practicum in Computing
  • Machine Learning
  • Computer Graphics
  • Computer Vision
Science Activities:
  • Program committee member of the some International Conferences
  • Reviewer and Sub-reviewer for some national journal, international conferences and international journals
Awards:
  • 2013 Sigweb DocEng Best Paper Award, Granted by ACM Symposium on Document Engineering
  • 2004 Exemplary female students in information technology
  • 2003 representative for talented and excellent young students of Vietnam National University

Publications

  1. Text Extraction Using Sparse Representation over Learning Dictionaries. In: Recent Trends in Image Processing and Pattern Recognition: Second International Conference, RTIP2R 2018, Solapur, India, December 21–22, 2018, Revised Selected Papers, Part III 2. Recent Trends in Image Processing and Pattern Recognition: Second International Conference, RTIP2R 2018, Solapur, India, December 21–22, 2018, Revised Selected Papers, Part III 2.; 2019. Available at: https://link.springer.com/chapter/10.1007/978-981-13-9187-3_1.
  2. DSD: document sparse-based denoising algorithm. Pattern Analysis and Applications . 2018.
  3. A New Approach for Traffic-Sign Recognition using Sparse Representation over Dictionary of Local Descriptors. In: The 9th International Conference on Knowledge and Systems Engineering . The 9th International Conference on Knowledge and Systems Engineering . Da Nang, VietNam: IEEE; 2017.
  4. Spotting Symbol over Graphical Documents via Sparsity in Visual Vocabulary. In: Recent Trends in Image Processing and Pattern Recognition (Part of CCIS book series, Vol 709, page 59-70). Recent Trends in Image Processing and Pattern Recognition (Part of CCIS book series, Vol 709, page 59-70). Springer ; 2016. doi:10.1007/978-981-10-4859-3.
  5. Sparse Representation over Learned Dictionary for Symbol Recognition. Signal Processing. 2016;125:36-47. doi:doi:10.1016/j.sigpro.2015.12.020.
  6. Spotting Symbol using Sparsity over Learned Dictionary of Local Descriptors. In: 11th International Workshop on Document Analysis Systems. 11th International Workshop on Document Analysis Systems. Tours, France: IEEE; 2014. doi:10.1109/DAS.2014.62.
  7. Document Noise Removal using Sparse Representation over Learned Dictionary . In: Proceedings of the 2013 ACM symposium on Document engineering. Proceedings of the 2013 ACM symposium on Document engineering. Florence, Italy: ACM New York, NY, USA; 2013. doi:10.1145/2494266.2494281.
  8. New Approach for Symbol Recognition Combining Shape Context of Interest Points with Sparse Representation. In: 12th International conference on Document Analysis and Recognition. 12th International conference on Document Analysis and Recognition. Washington DC: IEEE; 2013. doi:10.1109/ICDAR.2013.60.
  9. Text/graphic separation using a sparse representation with multi-learned dictionaries. In: 21st International conference on pattern recognition . 21st International conference on pattern recognition . Tsukuba, Japan : IEEE; 2012. Available at: https://ieeexplore.ieee.org/document/6460228/.
  10. Noise suppression over bi-level graphical documents using a sparse representation. In: Colloque International Francophone sur l’E ́crit et le Document - CIFED . Colloque International Francophone sur l’E ́crit et le Document - CIFED . Bordeaux, France; 2012.
Falculty of Mathematics, Mechanics and Informatics
VNU University of Science, 334 Nguyễn Trãi, Thanh Xuân, Hà Nội, Việt Nam
Office: (+84) 4 38 58 11 35, Computer lab: (+84) 4 38 58 15 30