1. dia

1
(No Transcript)
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
Our Problem

• The Problem Counting money.Input Photo of
  coins (forint with perspective view, without
  covering)(Lets 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.

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
Circular HT

• 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
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 sörözo
• Eat good and drink a lot
• Go back to the dormitory
• Go home with lots of new experiences, new
  remembrance

31
Other Works

• Coin DetectorCS7495/4495 Term ProjectDong-Shin
  Kim(gtg901p) CS7495Young Gyun Yun(gte257z)
  CS4495You-Kyung Cha(gte440y) CS4495
•  
• Dagobert A New Coin Recognition and Sorting
  SystemMichael Nolle1, Harald Penz2, Michael
  Rubik2,Konrad Mayer2, Igor Hollander2, Reinhard
  Granec2ARC Seibersdorf research GmbH1Video- and
  Safety Technology , 2High Performance Image
  ProcessingA-2444 Seibersdorf
• Design and Evaluation of Neural Networks for Coin
  Recognition by Using GA and SAYasue Mitsukura,
  Minoru Fukumi and Norio Akamatsu Department
  of Information Science Intelligent Systems,
  Faculty of EngineeringUniversity of Tokushima
  2-1, Minami-josanjima, Tokushima, 770-8506 JAPAN

32

• Thanks for your attention.

Questions?