Đỗ Thanh Hà

Đỗ Thanh Hà, Doctor
Position in Department:
Head of Department
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. DSD: document sparse-based denoising algorithm. Pattern Analysis and Applications . 2018.
  2. 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.
  3. Sparse Representation over Learned Dictionary for Symbol Recognition. Signal Processing. 2016;125:36-47. doi:doi:10.1016/j.sigpro.2015.12.020.
  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. 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.
  6. 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.
  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. 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.
  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. Parallelization of the combinational algorithms by the segmental method for solutions. In: The national conference on Information technology. The national conference on Information technology. Dong Nai, Vietnam; 2009.

Projects

Project Code Start date Title Status
January, 2019 Auto Segmentation the Organs over Medical Images Chưa nghiệm thu
QG.18.04 February, 2018 Denoising Images using Sparse Representation and Linear Regression Model Chưa nghiệm thu