A Case Study On Approximate FPGA Design With an Open-Source Image Processing Platform

2019 ◽  
Author(s):  
Yunxiang Zhang ◽  
Xiaokun Yang ◽  
Lei Wu ◽  
Jean H. Andrian
Keyword(s):  
Image Processing ◽  
Open Source ◽  
Source Image ◽  
Fpga Design ◽  
Processing Platform

scholarly journals Jenkins-CI, an Open-Source Continuous Integration System, as a Scientific Data and Image-Processing Platform

2016 ◽  
Vol 22 (3) ◽  
pp. 238-249 ◽  
Author(s):  
Ioannis K. Moutsatsos ◽  
Imtiaz Hossain ◽  
Claudia Agarinis ◽  
Fred Harbinski ◽  
Yann Abraham ◽  
...  
Keyword(s):  
Image Processing ◽  
Open Source ◽  
High Throughput Screening ◽  
High Performance ◽  
Heterogeneous Data ◽  
Scientific Data ◽  
Source Image ◽  
Integration System ◽  
Continuous Integration ◽  
Processing Platform

High-throughput screening generates large volumes of heterogeneous data that require a diverse set of computational tools for management, processing, and analysis. Building integrated, scalable, and robust computational workflows for such applications is challenging but highly valuable. Scientific data integration and pipelining facilitate standardized data processing, collaboration, and reuse of best practices. We describe how Jenkins-CI, an “off-the-shelf,” open-source, continuous integration system, is used to build pipelines for processing images and associated data from high-content screening (HCS). Jenkins-CI provides numerous plugins for standard compute tasks, and its design allows the quick integration of external scientific applications. Using Jenkins-CI, we integrated CellProfiler, an open-source image-processing platform, with various HCS utilities and a high-performance Linux cluster. The platform is web-accessible, facilitates access and sharing of high-performance compute resources, and automates previously cumbersome data and image-processing tasks. Imaging pipelines developed using the desktop CellProfiler client can be managed and shared through a centralized Jenkins-CI repository. Pipelines and managed data are annotated to facilitate collaboration and reuse. Limitations with Jenkins-CI (primarily around the user interface) were addressed through the selection of helper plugins from the Jenkins-CI community.

Image Processing using Open Source Tools and their Implementation in the Analysis of Complex Microstructures

2021 ◽  
Vol 58 (8) ◽  
pp. 484-506
Author(s):  
U. P. Nayak ◽  
M. Müller ◽  
D. Britz ◽  
M.A. Guitar ◽  
F. Mücklich
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
User Interface ◽  
Open Source ◽  
Microstructural Analysis ◽  
Microstructural Characterization ◽  
Source Image ◽  
Versatile Tool ◽  
Processing Software ◽  
Image Processing Software

Abstract Considering the dependance of materials’ properties on the microstructure, it is imperative to carry out a thorough microstructural characterization and analysis to bolster its development. This article is aimed to inform the users about the implementation of FIJI, an open source image processing software for image segmentation and quantitative microstructural analysis. The rapid advancement of computer technology in the past years has made it possible to swiftly segment and analyze hundreds of micrographs reducing hours’ worth of analysis time to a mere matter of minutes. This has led to the availability of several commercial image processing software programs primarily aimed at relatively inexperienced users. Despite the advantages like ‘one-click solutions’ offered by commercial software, the high licensing cost limits its widespread use in the metallographic community. Open-source platforms on the other hand, are free and easily available although rudimentary knowledge of the user-interface is a pre-requisite. In particular, the software FIJI has distinguished itself as a versatile tool, since it provides suitable extensions from image processing to segmentation to quantitative stereology and is continuously developed by a large user community. This article aims to introduce the FIJI program by familiarizing the user with its graphical user-interface and providing a sequential methodology to carry out image segmentation and quantitative microstructural analysis.

Accessing select properties of the electron with ImageJ: an open-source image-processing paradigm

2013 ◽  
Vol 35 (1) ◽  
pp. 015011 ◽  
Author(s):  
Junaid Alam ◽  
Amrozia Shaheen ◽  
Muhammad Sabieh Anwar
Keyword(s):  
Image Processing ◽  
Open Source ◽  
Source Image

scholarly journals Attendance Management System using Google Facenet for Facial Recognition

2021 ◽  
Vol 9 (VI) ◽  
pp. 5029-5036
Author(s):  
Rakesh Duggempudi
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Open Source ◽  
Management System ◽  
Facial Recognition ◽  
Cost Effective ◽  
Source Image ◽  
Student Attendance ◽  
Manual Work ◽  
View Update

Attendance management system is a required tool for attaining attendance in any habitat where attendance is essential. Yet, many of the available techniques consume time, are invasive and it demands manual work from the users. This research is directed at building a less invasive, cost effective and more efficient automated student attendance management system using face recognition that leverages on OpenCV functions for facial recognition. The system provides a GUI for marking attendance. It provides an interface for updating attendance using facial recognition libraries of OpenCV. The system stores attendance in a database which is maintained by the administrator. The administrator can view, update, and change the attendance of the students. The students can view and update their attendance. The system is developed on Open-Source image processing library and the interface is developed using Python Tkinter module. The Tkinter module is an open-source module by which we can develop GUI screens hence, it is not software dependent nor vendor hardware. The OpenCV module used for image processing is interfaced using python.

scholarly journals Nion Swift: Open Source Image Processing Software for Instrument Control, Data Acquisition, Organization, Visualization, and Analysis Using Python.

2019 ◽  
Vol 25 (S2) ◽  
pp. 122-123 ◽  
Author(s):  
Chris Meyer ◽  
Niklas Dellby ◽  
Jordan A. Hachtel ◽  
Tracy Lovejoy ◽  
Andreas Mittelberger ◽  
...  
Keyword(s):  
Image Processing ◽  
Data Acquisition ◽  
Open Source ◽  
Source Image ◽  
Control Data ◽  
Instrument Control ◽  
Processing Software ◽  
Image Processing Software

A Simple, Inexpensive, and Portable Image-Based Technique for Nondestructive Leaf Area Measurements

EDIS ◽  
2020 ◽  
Vol 2020 (6) ◽  
Author(s):  
Shinsuke Agehara ◽  
Lillian Pride ◽  
Mariel Gallardo ◽  
Jose Hernandez-Monterroza
Keyword(s):  
Image Processing ◽  
Open Source ◽  
Leaf Area ◽  
Leaf Growth ◽  
Source Image ◽  
Image Capture ◽  
Processing Program ◽  
Horticultural Research ◽  
Over Time

This new 6-page article, part of a series introducing various image-based measurements for horticultural research, introduces a simple, inexpensive, and portable image-based technique for nondestructive leaf area measurements. It uses an imaging apparatus made with ordinary office supplies to obtain leaf images in greenhouse or field environments. Leaf images are then processed and analyzed to measure leaf area using ImageJ, an open-source image processing program. Because both image capture and analysis are performed nondestructively, leaf area can be measured on the same leaf repeatedly, enabling the monitoring of leaf growth over time, as well as photosynthesis and transpiration. This technique is particularly useful to researchers and students studying leaf growth and physiology in greenhouse or field environments. Written by Shinsuke Agehara, Lillian Pride, Mariel Gallardo, and Jose Hernandez-Monterroza, and published by the UF/IFAS Horticultural Sciences Department.https://edis.ifas.ufl.edu/hs1395

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