A Classification Method for the Dirty Factor of Banknotes Based on Neural Network with Sine Basis Functions

He, Kexue; Peng, Shurong; Li, Shutao

doi:10.1109/icicta.2008.35

Cited by 8 publications

(26 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this system, the banknote types are classified by a three-layered perceptron, and banknote fitness is validated by radial basis function (RBF) networks [6]. For assessing the quality of Chinese banknotes (RMB), both the studies in [7,8] used the gray-level histogram of banknote images as the classification features, but employed different algorithms as the classifiers: neural network (NN) [7], and the combination of dynamic time warp (DTW) and support vector machine (SVM) [8]. Pham et al [9] proposed a fitness classification method for the Indian rupee (INR) based on grayscale images captured by visible light sensors.…”

Section: Related Workmentioning

confidence: 99%

“…Using the grayscale histogram of banknote images and classifying fitness using DTW and SVM [6] or using a NN [7]. -Using multiresolutional features of visible and IR images of banknote for recognition [8].…”

Section: Fitness Classification On Various National Currencies -mentioning

confidence: 99%

“…The first testing and second testing mean the same as those in Table 4 (unit: %). We also experimentally compared our proposed method to previous studies [2,7,9]. The two-fold cross-validation method was also adopted in these comparative experiments.…”

Section: Casementioning

confidence: 99%

“…This can be considered as equivalent to the VR1 experiment mentioned above. For the experiments using the method in [7], we extracted the histogram features from the grayscale VR images of the banknotes and classified the fitness levels using a multilayered perceptron (MLP) network with 95 nodes in the input and hidden layers. Referring to [9], we located the ROIs on the VR banknote images, performed Daubechies wavelet decomposition on the ROIs, and calculated the mean and standard variation values of the wavelet-transformed sub-bands.…”

Section: Casementioning

confidence: 99%

See 3 more Smart Citations

Deep Learning-Based Multinational Banknote Fitness Classification with a Combination of Visible-Light Reflection and Infrared-Light Transmission Images

Pham¹,

Nguyen²,

Kang³

et al. 2018

Symmetry

View full text Add to dashboard Cite

The fitness classification of a banknote is important as it assesses the quality of banknotes in automated banknote sorting facilities, such as counting or automated teller machines. The popular approaches are primarily based on image processing, with banknote images acquired by various sensors. However, most of these methods assume that the currency type, denomination, and exposed direction of the banknote are known. In other words, not only is a pre-classification of the type of input banknote required, but in some cases, the type of currency is required to be manually selected. To address this problem, we propose a multinational banknote fitness-classification method that simultaneously determines the fitness level of a banknote from multiple countries. This is achieved without the pre-classification of input direction and denomination of the banknote, using visible-light reflection and infrared-light transmission images of banknotes, and a convolutional neural network. The experimental results on the combined banknote image database consisting of the Indian rupee and Korean won with three fitness levels, and the United States dollar with two fitness levels, show that the proposed method achieves better accuracy than other fitness classification methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Fitness Classification On Various National Currencies -mentioning

confidence: 99%

Section: Casementioning

confidence: 99%

Section: Casementioning

confidence: 99%

See 2 more Smart Citations

Deep Learning-Based Multinational Banknote Fitness Classification with a Combination of Visible-Light Reflection and Infrared-Light Transmission Images

Pham¹,

Nguyen²,

Kang³

et al. 2018

Symmetry

View full text Add to dashboard Cite

show abstract

“…Obtaining grayscale histogram using the previous methods also omits necessary information regarding the dirty factor of banknotes [23,24]. The HSV colour space is a truer measure of colour in printed documents than RGB when converting to grayscale, as it separates the intensity (luminance), and colour information chromacity [25,26].…”

Section: A Featuresmentioning

confidence: 99%

Currency security and forensics: a survey

Chambers

Yan

Garhwal

et al. 2014

Multimed Tools Appl

View full text Add to dashboard Cite

Abstract-By its definition, the word 'currency' refers to an agreed medium for exchange, a nation's currency is the formal medium enforced by the elected governing entity. Throughout history, issuers have faced one common threat: counterfeiting. Despite technological advancements, overcoming counterfeit production remains a distant future. Scientific determination of authenticity requires a deep understanding of the raw materials and manufacturing processes involved. This survey serves as a synthesis of the current literature to understand the technology and the mechanics involved in currency manufacture and security, whilst identifying gaps in the current literature. Ultimately, a robust currency is desired.

show abstract

Parameters Learning of BPS M7 Banknote Processing Machine for Banknote Fitness Classification

Kongprasert

Chongstitvatana

2019

2019 23rd International Computer Science and Engineering Conference (ICSEC)

View full text Add to dashboard Cite

A Classification Method for the Dirty Factor of Banknotes Based on Neural Network with Sine Basis Functions

Cited by 8 publications

References 1 publication

Deep Learning-Based Multinational Banknote Fitness Classification with a Combination of Visible-Light Reflection and Infrared-Light Transmission Images

Deep Learning-Based Multinational Banknote Fitness Classification with a Combination of Visible-Light Reflection and Infrared-Light Transmission Images

Currency security and forensics: a survey

Parameters Learning of BPS M7 Banknote Processing Machine for Banknote Fitness Classification

Contact Info

Product

Resources

About