March 6th, 2010

Khai NguyenGrace ParkMatthew Pham

Nishanth AlapatiTrevor CarothersSky Lin

MENTOR: Jeff Wilhelm

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

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

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

twenty20

VLFeat – an open source library developed by grad students at UCLA Vision Lab

SIFT detects keypoints from reference images

Descriptors uniquely identify keypoints

Keypoints of new images are compared to the keypoints of our reference images to find a match

Robust to changes in scale, rotation

Previously used blob detection Extract image of currency value OCR to identify value

20

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

Constraints: requires the whole bill in view.

Sensitive to noise. Lack of resilience to rotation, scaling. Conclusion: Can’t compare to SIFT!

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

Precompute SIFT descriptors corresponding to template images.

More testing to refine SIFT parameters!