1. March 6 th, 2010 Khai Nguyen Grace Park Matthew Pham Nishanth Alapati Trevor Carothers Sky Lin MENTOR: Jeff Wilhelm

2.  Project Description  How It Works  Algorithm – SIFT  Algorithm – Blob Detection  Algorithm – Correlation  Conclusion  Demo  Future Work

3.  Create a mobile application that assists disabled people in identifying U.S. currency  The user will photograph bills and the application will say the denomination out loud

4.  The user takes a picture of a dollar bill  The application sends the picture to a server  The program on the server determines the denomination of the bill  The server returns the result to the phone, which says the denomination of the bill That’s a twenty 20

7.  VLFeat – an open source library developed by grad students at UCLA Vision Lab  SIFT detects keypoints from reference images  Descriptors uniquely identify keypoints

9.  Keypoints of new images are compared to the keypoints of our reference images to find a match  Robust to changes in scale, rotation

11.  Previously used blob detection  Extract image of currency value  OCR to identify value 20

12.  Limitation:  Can’t handle rotation.  Can’t handle both side of the bill  Can’t handle lighting effect of the bill

19.  Constraints: requires the whole bill in view.  Sensitive to noise.  Lack of resilience to rotation, scaling.  Conclusion: Can’t compare to SIFT!

20.  SIFT works best for currency recognition, due to its invariance to scale, rotation, and image quality.

22.  Precompute SIFT descriptors corresponding to template images.  More testing to refine SIFT parameters!