Coin Recognition with Reduced Feature Set SIFT Algorithm Using Neural Network

Farooque, Ghulam; Sargano, Allah Bux; Shafi, Imran; Ali, Waqar

doi:10.1109/fit.2016.025

Cited by 8 publications

(6 citation statements)

References 32 publications

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“…In addition, Farooque et al [34] have proposed a coin recognition system of Pakistani coins by using Scale Invariant Feature Transform (SIFT) algorithm and Principle Component Analysis (PCA) for feature extraction before output image is passed to ANN. The datasets consisting of 200 coins was converted to gray scale before extracting its features and the overall results showed 84% of accuracy.…”

Section: Coin Detection and Classification Systemmentioning

confidence: 99%

Deep Learning Based Malaysian Coins Recognition for Visual Impaired Person

Sufri

Rosidi

As’ari

2022

AEJ

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Currency recognition has been widely developed using various types of techniques and able to assist people who have a visual impairment. Machine learning is one of the methods implemented where deep learning architecture is one of them. The deep learning approach is reliable and can be used in detection and recognition of objects based on images. As currency recognition has been developed for other currencies, thus in this project, currency recognition using Malaysian coins has been developed by modeling Convolutional Neural Network (CNN) in recognizing coin images. Malaysian coins dataset was developed consist of 2400 images of four classes of coins, 5 sen, 10 sen, 20 sen, and 50 sen. In this study, pretrained CNN which are AlexNet, GoogleNet, and MobileNetV2 were formulated in recognizing such coins. Performance of each trained model was evaluated using confusion matrix and GoogleNet obtained the best performance with 99.2% testing accuracy, 99.2% precision, 99.18% recall, and 99.19% F1 score. From the trained model, it can be further developed and implemented in assisting visually impaired persons by producing a prototype using Raspberry Pi and FPGA before it can be clinically tested on the subject.

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Section: Coin Detection and Classification Systemmentioning

confidence: 99%

Deep Learning Based Malaysian Coins Recognition for Visual Impaired Person

Sufri

Rosidi

As’ari

2022

AEJ

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“…The flowchart of SIFT algorithm is shown in Figure 3. The first stage is constructing a scale-space (octave) using Gaussian blur using (1). L is a blurred image.…”

Section: B Sift Algorithmmentioning

confidence: 99%

“…There is a feature extraction stage in image or video data processing. The human eye can easily recognize an object, but the computer requires several features to process, such as the color, size, and shape of an object [1]. Object recognition using computers has developed in everyday life, including the recognition of aircraft and ships [2], recognition of butterflies, ants, cameras, and faces [3], and also currency recognition [4].…”

Section: Introductionmentioning

confidence: 99%

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Real-time currency recognition on video using AKAZE algorithm

Adhinata

Adhitama

Segara

2021

J. Teknol. dan Sist. Komput

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Currency recognition is one of the essential things since everyone in any country must know money. Therefore, computer vision has been developed to recognize currency. One of the currency recognition uses the SIFT algorithm. The recognition results are very accurate, but the processing takes a considerable amount of time, making it impossible to run for real-time data such as video. AKAZE algorithm has been developed for real-time data processing because of its fast computation time to process video data frames. This study proposes the faster real-time currency recognition system on video using the AKAZE algorithm. The purpose of this study is to compare the SIFT and AKAZE algorithms related to a real-time video data processing to determine the value of F1 and its speed. Based on the experimental results, the AKAZE algorithm is resulting F1 value of 0.97, and the processing speed on each video frame is 0.251 seconds. Then at the same video resolution, the SIFT algorithm results in an F1 value of 0.65 and a speed of 0.305 seconds to process one frame. These results show that the AKAZE algorithm is faster and more accurate in processing video data.

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“…A literatura já contempla a proposição de algumas soluções para esta problemática. O uso de algoritmos extratores de características com vistas a capturar informações a respeito de cor e textura para posterior classificação residem no cerne das principais contribuições [Jain andJain 2012, Roomi andRajee 2015], eventualmente também compreendendo redução de dimensionalidade [Farooque et al 2016]. Porém, resultados recentes e bastante notáveis das técnicas e modelos de Deep Learning têm residido naárea de VC, especialmente com o uso de Redes Neurais Convolucionais (CNNs, do inglês Convolutional Neural Networks), o que tem proporcionado um avanço no estado da arte [Khan et al 2018].…”

Section: Introductionunclassified

Uma Abordagem Híbrida para Detecção de Moedas de Real Baseada em Algoritmos de Localização e Redes Neurais Convolucionais

Yonekura¹,

Guedes²

2020

Anais Do Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2020)

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A detecção de múltiplas moedas é um problema de Visão Computacional que objetiva encontrar moedas em uma imagem e classificá-las quanto aos seus respectivos valores. Para o Real, a literatura tipicamente considera o uso de técnicas de extração de características seguida de classificadores de Machine Learning, em especial as redes neurais artificiais. Porém, considerando resultados recentes com Deep Learning, houve a motivação de propor uma abordagem híbrida considerando a localização de moedas com técnicas tradicionais de Visão Computacional e classificação com Redes Neurais Convolucionais. Múltiplas arquiteturas foram avaliadas e a solução proposta possui mean average precision superior a 92% com acurácia de classificação maior que 97%, superando outro resultado em 6%.

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Coin Recognition with Reduced Feature Set SIFT Algorithm Using Neural Network

Cited by 8 publications

References 32 publications

Deep Learning Based Malaysian Coins Recognition for Visual Impaired Person

Deep Learning Based Malaysian Coins Recognition for Visual Impaired Person

Real-time currency recognition on video using AKAZE algorithm

Uma Abordagem Híbrida para Detecção de Moedas de Real Baseada em Algoritmos de Localização e Redes Neurais Convolucionais

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