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Indian Coin Recognition and Sum Counting System of Image Data Mining Using Artificial Neural Networks
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- 발행기관
- 보안공학연구지원센터(IJAST) 바로가기
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- 간행물
- International Journal of Advanced Science and Technology 바로가기
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- 통권
- vol.31 (2011.06)바로가기
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- 페이지
- pp.67-80
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A147436
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- The objective of this paper is to classify recently released Indian coins of different denomination. The objective is to recognize the coins and count the total value of the coin in terms of Indian National Rupees (INR). The system designs coin recognition which uses by combining Robert’s edge detection method, Laplacian of Gaussion edge detection method, Canny edge detection method and Multi-Level Counter Propagation Neural Network (ML-CPNN) based on the coin Table 1. In this paper, it is proposed to introduce ML-CPNN approach. The features of old coins and new coins of different denominations are considered for classification. Indian Coins are released with different values and are classified based on different parameters of coin such as shape, size, surface, weight and so on. Some countries’ coins are having same parameters, but with different value. This paper concentrates on affine transformations such as simple gray level scaling, shearing, rotation etc. The coins are well recognized by zooming processes by which a coin size of the image is increased. To implement the coin classification, code is written in Matlab and tested with simulated results. A method is proposed for realizing a simple automatic coin recognition system more effectively. The Robert’s edge detection method gives 93% of accuracy and Laplacian of Gaussion method 95% of the result, the Canny edge detection method yields 97.25% result and the ML-CPNN approach yields 99.47% of recognition rate.
목차
Abstract
1. Introduction
1.1 Previous Works
1.2 Denomination of Indian Coins
2. Pattern Recognition
2.1. Coin Counting System
2.2. Pre-Processing
2.3. Data Acquisition
3. Extracting Features to Classify Labeled Coin Image
3.1 Coin Segmentation and Labeling
3.2 Edge Detection
4. Multi-Level Counter Propagation Neural Network (ML-CPNN)
4.1. Implementation procedure
5. Conclusion and Results
References
키워드
Smoothing Edge detection Thresholding Recognition Classification.저자
간행물 정보
발행기관
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- 발행기관명
- 보안공학연구지원센터(IJAST) [Science & Engineering Research Support Center, Republic of Korea(IJAST)]
- 설립연도
- 2006
- 분야
- 공학>컴퓨터학
- 소개
- 1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업
간행물
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- 간행물명
- International Journal of Advanced Science and Technology
- 간기
- 월간
- pISSN
- 2005-4238
- 수록기간
- 2008~2016
- 십진분류
- KDC 505 DDC 605
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