scholarly journals System for Fake Currency Detection Using Image Processing

2022 ◽  
Vol 10 (1) ◽  
pp. 349-352
Author(s):  
Prof. F. S. Ghodichor
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Global Economy ◽  
Ordinary People ◽  
Python Programming Language ◽  
Technological Growth ◽  
Average Person ◽  
Growth Development ◽  
Python Programming ◽  
To Receive

Abstract: Counterfeit money has always existed an issue that has caused many problems in the market. Technological growth development has made it possible to create extra counterfeit items which are distributed in the mitigation market the global economy. Bangui existing banking equipment and so on trading sites to check the authenticity of funds. But the average person does not do that have access to such systems and that is why they are needed in order for the software to receive counterfeit money, which can be used by ordinary people. This the proposed system uses image processing to find out if the money is real or fake. System built uses the Python system completely language. It contains similar steps grayscale modification, edge detection, separation, etc. made using appropriate methods. Keyword: Counterfeit currency, Image Processing, Python programming language, grayscale conversion, edge detection, segmentation.

scholarly journals Machine Learning-Based Technique for Image Classification and Identification of Similarity for Commonness of Source

2020 ◽  
Vol 9 (6) ◽  
pp. 544-548
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Edge Detection ◽  
Image Classification ◽  
Programming Language ◽  
Python Programming Language ◽  
Reserve Bank ◽  
Python Programming ◽  
Highlight Extraction ◽  
Reserve Bank Of India

In India Every year RBI (Reserve bank of India) faces the issue of fake currency. Fake Currency has consistently been an issue that has made a lot of chaos in the market. The expanding mechanical progressions have made the opportunities for making progressively fake currency which is circled in the market which decreases the general economy of the nation. There are machines present at banks and other business regions to check the validness of the monetary forms. Be that as it may, a typical man doesn't approach such frameworks and henceforth a requirement for a product to distinguish counterfeit cash emerges, which can be utilized by average folks. This proposed framework utilizes Image Processing to identify whether the currency is real or fake. The framework is structured utilizing Python programming language and OpenCV. It comprises of the means, for example, grayscale detection, edge detection, Highlight Extraction, and so forth which are performed utilizing reasonable strategies. And which will be further implemented in the Framework for Classification and Identification of Similarity for Commonness of Source

scholarly journals scikit-image: Image processing in Python

2014 ◽  
Author(s):  
Stéfan van der Walt ◽  
Johannes L Schönberger ◽  
Juan Nunez-Iglesias ◽  
François Boulogne ◽  
Joshua D Warner ◽  
...  
Keyword(s):  
Image Processing ◽  
Open Source ◽  
Programming Language ◽  
Real World ◽  
Research Education ◽  
Python Programming Language ◽  
Image Library ◽  
Industry Applications ◽  
Python Programming ◽  
World Image

scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal "Modified BSD" open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators. In this paper we highlight the advantages of open source to achieve the goals of the scikit-image library, and we showcase several real-world image processing applications that use scikit-image.

scholarly journals DESENVOLVIMENTO DE UM DISPOSITIVO PARA APOIO AO ENSINO DE COMPUTAÇÃO E ROBÓTICA

2014 ◽  
Vol 6 (2) ◽  
pp. 71-85
Author(s):  
Rafael de Oliveira Maia ◽  
Francisco Assis da Silva ◽  
Mário Augusto Pazoti ◽  
Leandro Luiz de Almeida ◽  
Danillo Roberto Pereira
Keyword(s):  
Image Processing ◽  
Computer Science ◽  
Programming Language ◽  
Raspberry Pi ◽  
Writing Programs ◽  
Python Programming Language ◽  
Python Programming ◽  
And Robotics

In this work we proposed the development of an alternative device as a motivating element to learn computer science and robotics using the Raspberry PI and Arduino boards. The connections of all hardware used to build the device called Betabot are presented and are also reported the technologies used for programming the Betabot. An environment for writing programs to run at Betabot was developed. With this environment it is possible to write programs in the Python programming language, using libraries with functions specific to the device. With the Betabot using a webcam and through image processing search for patterns like faces, circles, squares and colors. The device also has functions to move servos and motors, and capture values returned by some kindsof sensors connected to communication ports. From this work, it was possible to develop a device that is easy to be manipulated and programmed, which can be used to support the teaching of computer science and robotics.

scholarly journals SciPy and OpenCV as an interactive computing environment for computer vision

2015 ◽  
Vol 22 (1) ◽  
pp. 154
Author(s):  
Thiago Teixeira Santos
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Computer Vision ◽  
Vision Research ◽  
Data Exploration ◽  
Computing Environment ◽  
Interactive Computing ◽  
Python Programming Language ◽  
Computing Environments ◽  
Python Programming

In research and development (R&D), interactive computing environments are a frequently employed alternative for data exploration, algorithm development and prototyping. In the last twelve years, a popular scientific computing environment flourished around the Python programming language. Most of this environment is part of (or built over) a software stack named SciPy Stack. Combined with OpenCV’s Python interface, this environment becomes an alternative for current computer vision R&D. This tutorial introduces such an environment and shows how it can address different steps of computer vision research, from initial data exploration to parallel computing implementations. Several code examples are presented. They deal with problems from simple image processing to inference by machine learning. All examples are also available as IPython notebooks.

scholarly journals Detection of Fake Currency using Image Processing

2021 ◽  
Vol 9 (VII) ◽  
pp. 419-423
Author(s):  
Ashik Shiby
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Programming Language ◽  
Money Laundering ◽  
National Currency ◽  
Ordinary People ◽  
Research Project ◽  
Legal Entity ◽  
Grey Scale ◽  
Image Separation

In its definition, the term 'currency' defines an agreed-upon exchange item, the national currency being the legal entity used by the selected controlling entity. Throughout history, issuers have faced 1 common threat: counterfeit. In recent years fake money note has been printed that has resulted in significant losses and damage to society. Therefore, it becomes necessary to build a tool for earning money. This research project proposes a way to look at the note of counterfeit money distributed in our country through their image. After selecting an image use pre-processing. In pre-processing, the acquired image is cropped, smooth, and adjust. Change the image to grey-scale. After conversion use image separation. Features are extracted and reduce. Finally, compare the picture to be real or fake. Duplicate money has been a major problem in the market. There are currency counting machines available in banks and other trading venues to check financial authenticity. Most people do not have access to such programs which is why there is a need for fake money laundering software, which can be used by ordinary people. This proposed framework uses Image Processing to determine whether the money is real or counterfeit. The research project program is built entirely using Python's programming language. It has the methods such as grayscale conversion, edge detection, segmentation, etc.

scholarly journals Object Detection with a Webcam Using the Python Programming Language

2021 ◽  
Vol 2 (2) ◽  
pp. 103-111
Author(s):  
Ali Basrah Pulungan ◽  
Zhafranul Nafis ◽  
Muhammad Anwar ◽  
Hastuti ◽  
Hamdani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Object Recognition ◽  
Object Detection ◽  
Programming Language ◽  
Python Programming Language ◽  
Detection Of Objects ◽  
The Times ◽  
Python Programming ◽  
Shape And Size

Technology developed rapidly along the times, various ways are done to make works easier. One of them is by utilizing artificial intelligence, likes the use of a webcam as a sensor in detecting an object through several stages of image processing. There was research on object recognition previously using three measurement parameters based on color, shape, and size of objects. It is used a webcam as the sensing sensor, and image processing is processed with python programming. In this article, writer described about image processing for the detection of objects used in the research. The results of this device have been tested and are able to detect objects properly based on predetermined color, shape and size. Object detection using a webcam can work properly according to what the author wants.

scholarly journals scikit-image: Image processing in Python

2014 ◽  
Author(s):  
Stéfan van der Walt ◽  
Johannes L Schönberger ◽  
Juan Nunez-Iglesias ◽  
François Boulogne ◽  
Joshua D Warner ◽  
...  
Keyword(s):  
Image Processing ◽  
Open Source ◽  
Programming Language ◽  
Real World ◽  
Research Education ◽  
Python Programming Language ◽  
Image Library ◽  
Industry Applications ◽  
Python Programming ◽  
World Image

scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal "Modified BSD" open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators. In this paper we highlight the advantages of open source to achieve the goals of the scikit-image library, and we showcase several real-world image processing applications that use scikit-image.

scholarly journals Sistem Pergerakan Robot Kiper Beroda Menggunakan Metode Wall Follower Berbasis Image Processing

ELKHA ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 1
Author(s):  
Risfendra Risfendra ◽  
Asfinaldi Asfinaldi ◽  
Habibullah Habibullah ◽  
Julisardi Julisardi
Keyword(s):  
Image Processing ◽  
Programming Language ◽  
Input Data ◽  
Analysis Data ◽  
Efficiency Ratio ◽  
Soccer Robot ◽  
Robot System ◽  
Actual Distance ◽  
Python Programming Language ◽  
Python Programming

One of Indonesian Robot Contest divisions is the Indonesia wheeled soccer robot contest. There are three players called the striker, defense and goalkeeper robot, which is drived by wheels that controlled based on three aferomentioned positions. This study aims build the goalkeeper robot equipped with image processing to detect the ball using a camera sensor that installed in the the robot system. The Image processing contructed using the python programming language with OpenCV library. The results of image processing are used as input data that controlled by Arduino Mega 2560, which is connected serially to the PC's USB port. The results shows the maximum linear velocity that can be achieved is 1.59 m/s. Furthermore, the efficiency ratio of analysis data to the actual distance is 86.77 %

scholarly journals scikit-image: Image processing in Python

2014 ◽  
Author(s):  
Stéfan van der Walt ◽  
Johannes L Schönberger ◽  
Juan Nunez-Iglesias ◽  
François Boulogne ◽  
Joshua D Warner ◽  
...  
Keyword(s):  
Image Processing ◽  
Open Source ◽  
Programming Language ◽  
Real World ◽  
Research Education ◽  
Python Programming Language ◽  
Image Library ◽  
Industry Applications ◽  
Python Programming ◽  
World Image

scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal "Modified BSD" open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators. In this paper we highlight the advantages of open source to achieve the goals of the scikit-image library, and we showcase several real-world image processing applications that use scikit-image.

A Python application to generate digital signals

2020 ◽  
Vol 65 (1) ◽  
pp. 96-104
Author(s):  
Tatian-Cristian Mălin
Keyword(s):  
Numerical Simulation ◽  
Programming Language ◽  
Digital Signals ◽  
Python Programming Language ◽  
Python Programming

We introduce in this paper an application developed in the Python programming language that can be used to generate digital signals with known frequencies and amplitudes. These digital signals, since have known parameters, can be used to create benchmarks for test and numerical simulation.

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