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 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 End-To-End Computer Vision Framework: An Open-Source Platform for Research and Education

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3691
Author(s):  
Ciprian Orhei ◽  
Silviu Vert ◽  
Muguras Mocofan ◽  
Radu Vasiu
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Computer Vision ◽  
Open Source ◽  
Visual Processing ◽  
Research Field ◽  
Learning Models ◽  
Research Activity ◽  
End To End ◽  
Machine Learning Models

Computer Vision is a cross-research field with the main purpose of understanding the surrounding environment as closely as possible to human perception. The image processing systems is continuously growing and expanding into more complex systems, usually tailored to the certain needs or applications it may serve. To better serve this purpose, research on the architecture and design of such systems is also important. We present the End-to-End Computer Vision Framework, an open-source solution that aims to support researchers and teachers within the image processing vast field. The framework has incorporated Computer Vision features and Machine Learning models that researchers can use. In the continuous need to add new Computer Vision algorithms for a day-to-day research activity, our proposed framework has an advantage given by the configurable and scalar architecture. Even if the main focus of the framework is on the Computer Vision processing pipeline, the framework offers solutions to incorporate even more complex activities, such as training Machine Learning models. EECVF aims to become a useful tool for learning activities in the Computer Vision field, as it allows the learner and the teacher to handle only the topics at hand, and not the interconnection necessary for visual processing flow.

scholarly journals Image classification using Deep learning

2018 ◽  
Vol 7 (2.7) ◽  
pp. 614 ◽  
Author(s):  
M Manoj krishna ◽  
M Neelima ◽  
M Harshali ◽  
M Venu Gopala Rao
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Classical Problem

The image classification is a classical problem of image processing, computer vision and machine learning fields. In this paper we study the image classification using deep learning. We use AlexNet architecture with convolutional neural networks for this purpose. Four test images are selected from the ImageNet database for the classification purpose. We cropped the images for various portion areas and conducted experiments. The results show the effectiveness of deep learning based image classification using AlexNet.  

scholarly journals An overview of two open interactive computing environments useful for data science education

JAMIA Open ◽  
2018 ◽  
Vol 1 (2) ◽  
pp. 159-165
Author(s):  
Robert Hoyt ◽  
Victoria Wangia-Anderson
Keyword(s):  
Science Education ◽  
Programming Languages ◽  
Data Science ◽  
Predictive Analytics ◽  
Data Exploration ◽  
Online Data ◽  
Education Benefits ◽  
Interactive Computing ◽  
Data Analyses ◽  
Computing Environments

Abstract Objective To discuss and illustrate the utility of two open collaborative data science platforms, and how they would benefit data science and informatics education. Methods and Materials The features of two online data science platforms are outlined. Both are useful for new data projects and both are integrated with common programming languages used for data analysis. One platform focuses more on data exploration and the other focuses on containerizing, visualization, and sharing code repositories. Results Both data science platforms are open, free, and allow for collaboration. Both are capable of visual, descriptive, and predictive analytics Discussion Data science education benefits by having affordable open and collaborative platforms to conduct a variety of data analyses. Conclusion Open collaborative data science platforms are particularly useful for teaching data science skills to clinical and nonclinical informatics students. Commercial data science platforms exist but are cost-prohibitive and generally limited to specific programming languages.

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.

MACHINE LEARNING APPLICATION: IMAGE PROCESSING

2021 ◽  
Vol 6 (3) ◽  
Author(s):  
Ayush Gupta
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Computer Vision ◽  
Early Stage ◽  
Human Beings ◽  
Significant Information ◽  
Helpful Tool ◽  
Early Stages ◽  
Literature Reviews ◽  
Current Scenario

-In the current scenario of the data world, the data holds significant information if processed correctly. The data can be in the form of images which can prove to be a boon in deriving the useful insights from it in order to get the knowledge of things at an early stage itself. But the matter of concern is deriving the information from the images will be a tedious task for human beings and would incur a heavy cost and time. So, an easy and cheaper technique is to teach a machine efficiently to do the task for us. The concept of using Machines to do human tasks is known as Machine Learning. In this paper, I present various literature reviews regarding image processing in Machine learning and how image processing has helped in identifying the issues at early stages so that they can be resolved easily without causing much harm. Also, image processing has been a helpful tool in computer vision.

A Scientometric Analysis and a Review on Current Literature of Computer Vision Applications

2020 ◽  
pp. 104-117
Author(s):  
Osman Hürol Türkakın
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Computer Vision ◽  
Current Literature ◽  
Machine Learning Techniques ◽  
Scientometric Analysis ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Computer Vision Applications ◽  
Processing Techniques

Computer vision methods are wide-spread techniques mostly used for detecting cracks on structural components, extracting information from traffic flows, and analyzing safety in construction processes. In recent years, with increasing usage of machine learning techniques, computer vision applications are supported by machine learning approaches. So, several studies were conducted using machine learning techniques to apply image processing. As a result, this chapter offers a scientometric analysis for investigating current literature of image processing studies for civil engineering field in order to track the scientometric relationship between machine learning and image processing techniques.

Machine Learning Made Easy: A Review of Scikit-learn Package in Python Programming Language

2019 ◽  
Vol 44 (3) ◽  
pp. 348-361 ◽  
Author(s):  
Jiangang Hao ◽  
Tin Kam Ho
Keyword(s):  
Machine Learning ◽  
Programming Languages ◽  
Programming Language ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
The Past ◽  
Machine Learning Methods ◽  
Python Programming Language ◽  
And Behavior ◽  
Python Programming

Machine learning is a popular topic in data analysis and modeling. Many different machine learning algorithms have been developed and implemented in a variety of programming languages over the past 20 years. In this article, we first provide an overview of machine learning and clarify its difference from statistical inference. Then, we review Scikit-learn, a machine learning package in the Python programming language that is widely used in data science. The Scikit-learn package includes implementations of a comprehensive list of machine learning methods under unified data and modeling procedure conventions, making it a convenient toolkit for educational and behavior statisticians.

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