Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A camera phone based currency reader for the visually impaired
Proceedings of the 10th international ACM SIGACCESS conference on Computers and accessibility
Pose tracking from natural features on mobile phones
ISMAR '08 Proceedings of the 7th IEEE/ACM International Symposium on Mixed and Augmented Reality
Proceedings of the 11th international ACM SIGACCESS conference on Computers and accessibility
Blind-folded recognition of bank notes on the mobile phone
ACM SIGGRAPH 2010 Posters
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Despite the rapidly increasing use of credit cards and other electronic forms of payment, cash is still widely used for everyday transactions due to its convenience, perceived security and anonymity. However, the visually impaired might have a hard time telling each paper bill apart, since, for example, all dollar bills have the exact same size and, in general, currency bills around the world are not distinguishable by any tactile markings. We propose the use of a broadly available tool, the camera of a smart-phone, and an adaptation of the SIFT algorithm to recognize partial and even distorted images of paper bills. Our algorithm improves memory efficiency and the speed of SIFT key-point classification by using a k-means clustering approach. Our results show that our system can be used in real-world scenarios to recognize unknown bills with a high accuracy.