Conference Paper2014Cited by 9

Recognizing Bangladeshi currency for visually impaired

Mohammad M Rahman, Bruce Poon, M Ashraful Amin, Hong Yan

Machine Learning and Cybernetics: 13th International Conference, Lanzhou, China, July 13-16, 2014. Proceedings 13

Springer Berlin Heidelberg, pp. 129–135, ISBN: 9783662456514

  • Reduced Inequalities — Relevance: 83%
DOI: 10.1007/978-3-662-45652-1_14URL

Keywords

  • Assistive technology
  • banknote recognition
  • Pattern Recognition
  • image matching
  • complex background
  • visually impaired people
  • real-time image analysis

CCDS Authors

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

References

  1. 1.David Lowe. (2004). Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 60(2), 91–110[10.1023/b:visi.0000029664.99615.94]
  2. 2.Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van Gool. (2008). Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding, 110(3), 346–359[10.1016/j.cviu.2007.09.014]
  3. 3.Edward Rosten, Tom Drummond. (2006). Machine Learning for High-Speed Corner Detection. Lecture notes in computer science, 430–443[10.1007/11744023_34]
  4. 4.Michael Calonder, Vincent Lepetit, Christoph Strecha, Pascal Fua. (2010). BRIEF: Binary Robust Independent Elementary Features. Lecture notes in computer science, 778–792[10.1007/978-3-642-15561-1_56]
  5. 5.Bogdan Alexe, Thomas Deselaers, Vittorio Ferrari. (2010). ClassCut for Unsupervised Class Segmentation. Lecture notes in computer science, 380–393[10.1007/978-3-642-15555-0_28]
  6. 6.Arcangelo Bruna, Giovanni Maria Farinella, Giuseppe Claudio Guarnera, Sebastiano Battiato. (2013). Forgery Detection and Value Identification of Euro Banknotes. Sensors, 13(2), 2515–2529[10.3390/s130202515]

Cited By

  1. 1.Md. Ferdousur Rahman Sarker, Md. Israfil Mahmud Raju, Ahmed Al Marouf, Rubaiya Hafiz, Syed Akhter Hossain, Munim Hossain Khandker Protik. (2019). Real-time Bangladeshi Currency Detection System for Visually Impaired Person. , 1–4[10.1109/icbslp47725.2019.201518]
  2. 2.P. Selvi Rajendran, Dr. T. P. Anithaashri. (2020). RETRACTED: CNN based framework for identifying the Indian currency denomination for physically challenged people. IOP Conference Series Materials Science and Engineering, 992(1), 012016[10.1088/1757-899x/992/1/012016]
  3. 3.Hasan Murad, Nafis Irtiza Tripto, Mohammad Eunus Ali. (2019). Developing a bangla currency recognizer for visually impaired people. , 1–5[10.1145/3287098.3287152]
  4. 4.Tuyen Pham, Young Park, Seung Kwon, Dat Nguyen, Husan Vokhidov, Kang Park, Dae Jeong, Sungsoo Yoon. (2015). Recognizing Banknote Fitness with a Visible Light One Dimensional Line Image Sensor. Sensors, 15(9), 21016–21032[10.3390/s150921016]
  5. 5.Tutil Kazi, Kingkar Prosad Ghosh, Tanjina Akter. (2024). Classification of Bangladeshi Currency Using Convolutional Neural Network in Cross-Dataset Recognition Environment. , 196–201[10.1109/iceeict62016.2024.10534499]
  6. 6.Suriporn Angsupanich, Sureena Matayong. (2023). A prototype real-time mobile application for Thai banknote recognition to support visually impaired persons. Universal Access in the Information Society, 23(4), 1987–2002[10.1007/s10209-023-01016-8]
  7. 7.Ali Hasan Md. Linkon, Md. Mahir Labib, Faisal Haque Bappy, Soumik Sarker, Marium-E Jannat, Md Saiful Islam. (2020). Deep Learning Approach Combining Lightweight CNN Architecture with Transfer Learning: An Automatic Approach for the Detection and Recognition of Bangladeshi Banknotes. , 214–217[10.1109/icece51571.2020.9393113]
  8. 8.Yash Chauhan, Prateek Singh. (2021). A Novel Convolutional Neural Network for Classifying Indian Coins by Denomination. [10.31224/osf.io/znxrg]
  9. 9.Md. Shanjinur Islam, Ashikur Rahman. (2021). iNote: A Low Cost Banknote Recognition System for Visually Impaired Persons. Lecture notes in networks and systems, 165–176[10.1007/978-3-030-75078-7_18]
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