A NEURO FUZZY BASED COIN RECOGNITION WITH COLLABORATION OF MULTI-FEATURE EXTRACTION TECHNIQUES

Authors

  • Sukhwinder Kaur Computer Science Engineering, Ramgarhia Institute of Engineering and Technology, IKGPTU
  • Er. Harshdeep Trehan (A.P) Computer Science Engineering, Ramgarhia Institute of Engineering and Technology , IKGPTU
  • Er. Varinderjit Kaur (HOD) Computer Science Engineering, Ramgarhia Institute of Engineering and Technology, IKGPTU
  • Dr. Naveen Dhillon (Principal) Computer Science Engineering, Ramgarhia Institute of Engineering and Technology, IKGPTU

Keywords:

Image based coin recognition, Gabor Wavelet, Canny Edge Detection, Artificial Neuro Fuzzy Inference System( ANFIS)

Abstract

Coin has a major role in daily life of a human as it is used in various financial organization or institutes like
banks, supermarket and vending machines etc. Along with the enhancements in the technology, most of the human works
get rely upon machines. Similarly, the coin recognition is also done by machines or software since it is mandatory to
recognize the coins rather than counting coins manually. The coin recognition is done by using image preprocessing
technique. For this purpose, various techniques have been developed by different authors. This study provides a coin
recognition system by using Gabor Wavelet, Canny Edge detection and Artificial Neuro Fuzzy Inference System (ANFIS).
The Gabor-ANFIS is evaluated to be the best coin recognition mechanism than Scale Invariant Feature TransformArtificial Neural Network (SIFT-ANN) with respect to Accuracy and Error percentage.

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Published

2018-04-25

How to Cite

Sukhwinder Kaur, Er. Harshdeep Trehan (A.P), Er. Varinderjit Kaur (HOD), & Dr. Naveen Dhillon (Principal). (2018). A NEURO FUZZY BASED COIN RECOGNITION WITH COLLABORATION OF MULTI-FEATURE EXTRACTION TECHNIQUES. International Journal of Advance Engineering and Research Development (IJAERD), 5(4), 2526–2531. Retrieved from https://www.ijaerd.org/index.php/IJAERD/article/view/3383

Issue

Vol. 5 No. 4 (2018): Volume 5 Issue 4 April 2018

Section

Articles