2. Money, Money, Money TEAM 6

3. The TEAM Dana Damian Scientist Institute: Politehnica University of Timisoara Country: Romania Krisztina Dombi Documenter Institute: University of Szeged Country: Hungary Levente Sajó Programmer Institute: University of Debrecen Country: Hungary Zoltán Horváth Gopher Institute: Pannon University Country: Hungary

4. The Problem The Problem: Counting money. Input: Photo of coins (Euro\Cent perspective view, non-uniform lighting, eventual partial covering) Task: Recognize the coins and count the total sum. Output: The sum, and also the recognition statistics (accuracy / false positive rate etc) of the implemented method. Difficulty: Medium

5. The Problem: Counting money. Input: Photo of coins (forint with perspective view, without covering) (Let’s say we have a lot…) Task: Recognize the coins and count the total sum. Output: The sum, and also the recognition statistics (accuracy / false positive rate etc) of the implemented method. Our Problem

6. Motivation In business transactions, to enable computers to recognize coins and other different forms of currency has become an essential process. If computers are able to do the recognition, all monetary trades and transactions will be much easier. Our scope is limited on recognizing only the Hungarian coins ( head OR tail ) (1F, 2F, 10F, 20F, 50F, 100F).

7. Monetary automates

8. Handy coin counter

9. Approach The application is suitable for an architecture of a coin counter system that incorporates a steady camera which monitories coins passing beneath (maybe on a belt )

10. Catalogue of Hungarian denomination

11. Theoretical background of Hough transformation A transformation that maps a point in a Cartesian space onto a 2D space of points, called the Hough Space

12. Extension of the classical HT Analytical function of a circle leads to a mapping of each point (x, y) from the image onto a 3D Hough Space parameterized according to (a, b, r) tuple, where –(a, b)  center of the circle –r  radius of the center Points satisfying the equation are mapped into the accumulator according to the circle they belong to Circular HT

13. Preprocessing Enhance Contrast Sharpen Gaussian Blur Sharpen Find Edges Threshold Fill Holes Outline Invert

14. Hungarian coin counter system Input image:

15. Enhance Contrast

16. Sharpen

17. Gaussian Blur

18. Edge Detector

19. Threshold

20. Fill Holes

21. Outline

22. Invert binary

23. Circular Hough Transform

24. Detected coins

25. Center points and radius

26. Result

27. Core Idea Having a picture for training purposes, the system designs a coin table in which it stores the size of each coin Further recognition is based on comparison with the coin table

28. Main issues Shadows can enlarge the image of a coin, thus increasing its radius Different condition of illumination can generate an edge map with lack of information Coins are very close to each other

29. Limitations A priori knowledge of the # coins Dependence on the quality of edge detector

30. Future Plans Go to the Bajor söröző Eat good and drink a lot Go back to the dormitory Go home with lots of new experiences, new remembrance

31. Other Works Coin Detector CS7495/4495 Term Project Dong-Shin Kim(gtg901p) CS7495 Young Gyun Yun(gte257z) CS4495 You-Kyung Cha(gte440y) CS4495Coin Detector Dagobert – A New Coin Recognition and Sorting System Michael N¨olle1, Harald Penz2, Michael Rubik2, Konrad Mayer2, Igor Holl¨ander2, Reinhard Granec2 ARC Seibersdorf research GmbH 1Video- and Safety Technology, 2High Performance Image Processing A-2444 SeibersdorfDagobert Design and Evaluation of Neural Networks for Coin Recognition by Using GA and SA Yasue Mitsukura*, Minoru Fukumi* and Norio Akamatsu* * Department of Information Science & Intelligent Systems, Faculty of Engineering University of Tokushima 2-1, Minami-josanjima, Tokushima, 770-8506 JAPANDesign and Evaluation of Neural Networks for Coin Recognition by Using GA and SA

32. Thanks for your attention. Questions? …