scholarly journals V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets

2010 ◽  
Vol 28 (4) ◽  
pp. 348-353 ◽  
Author(s):  
Hanchuan Peng ◽  
Zongcai Ruan ◽  
Fuhui Long ◽  
Julie H Simpson ◽  
Eugene W Myers
Keyword(s):  
Quantitative Analysis ◽  
Real Time ◽  
Large Scale ◽  
3D Visualization ◽  
Image Data ◽  
Data Sets ◽  
Biological Image

scholarly journals An Interactive 3D Visualization Tool for Large Scale Data Sets for Quantitative Atom Probe Tomography

2011 ◽  
Vol 1349 ◽  
Author(s):  
Hari Dahal ◽  
Michael Stukowski ◽  
Matthias J. Graf ◽  
Alexander V. Balatsky ◽  
Krishna Rajan
Keyword(s):  
User Interface ◽  
Open Source ◽  
Real Time ◽  
Atom Probe Tomography ◽  
Large Scale ◽  
3D Visualization ◽  
Atom Probe ◽  
Parallel Rendering ◽  
Data Sets ◽  
Process Data

ABSTRACTSeveral visualization schemes have been developed for imaging materials at the atomic level through atom probe tomography. The main shortcoming of these tools is their inability to parallel process data using multi-core computing units to tackle the problem of larger data sets. This critically handicaps the ability to make a quantitative interpretation of spatial correlations in chemical composition, since a significant amount of the data is missed during subsequent analysis. In addition, since these visualization tools are not open-source software there is always a problem with developing a common language for the interpretation of data. In this contribution we present results of our work on using an open-source advanced interactive visualization software tool, which overcomes the difficulty of visualizing larger data sets by supporting parallel rendering on a graphical user interface or script user interface and permits quantitative analysis of atom probe tomography data in real time. This advancement allows materials scientists a codesign approach to making, measuring and modeling new and nanostructured materials by providing a direct feedback to the fabrication and designing of samples in real time.

A Real-Time Log Analyzer Based on MongoDB

2014 ◽  
Vol 571-572 ◽  
pp. 497-501 ◽  
Author(s):  
Qi Lv ◽  
Wei Xie
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Performance Comparison ◽  
Log Analysis ◽  
Data Sets ◽  
Time Data ◽  
Real Time Analysis ◽  
Large Scale Data ◽  
Implementation Approach ◽  
And Performance

Real-time log analysis on large scale data is important for applications. Specifically, real-time refers to UI latency within 100ms. Therefore, techniques which efficiently support real-time analysis over large log data sets are desired. MongoDB provides well query performance, aggregation frameworks, and distributed architecture which is suitable for real-time data query and massive log analysis. In this paper, a novel implementation approach for an event driven file log analyzer is presented, and performance comparison of query, scan and aggregation operations over MongoDB, HBase and MySQL is analyzed. Our experimental results show that HBase performs best balanced in all operations, while MongoDB provides less than 10ms query speed in some operations which is most suitable for real-time applications.

Let the Data Do the Talking: Hypothesis Discovery from Large-Scale Data Sets in Real Time

2012 ◽  
pp. 235-257
Author(s):  
Christopher Oehmen ◽  
Scott Dowson ◽  
Wes Hatley ◽  
Justin Almquist ◽  
Bobbie-Jo Webb-Robertson ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Data Sets ◽  
Large Scale Data ◽  
Scale Data ◽  
Large Scale Data Sets

scholarly journals Real Time System for Human Identification and Tracking from Surveillance Videos

2018 ◽  
Vol 7 (3.12) ◽  
pp. 244 ◽  
Author(s):  
D Vishaka Gayathri ◽  
Shrutee Shree ◽  
Taru Jain ◽  
K Sornalakshmi
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Training Data ◽  
Live Streaming ◽  
Data Sets ◽  
Surveillance Systems ◽  
Real Time System ◽  
Detection Techniques ◽  
Current Location ◽  
Viable System

The need for intelligent surveillance systems has raised the concerns of security. A viable system with automated methods for person identification to detect, track and recognize persons in real time is required. The traditional detection techniques have not been able to analyze such a huge amount of live video generated in real-time. So, there is a necessity for live streaming video analytics which includes processing and analyzing large scale visual data such as images or videos to find content that are useful for interpretation. In this work, an automated surveillance system for real-time detection, recognition and tracking of persons in video streams from multiple video inputs is presented. In addition, the current location of an individual can be searched with the tool bar provided. A model is proposed, which uses a messaging queue to receive/transfer video feeds and the frames in the video are analyzed using image processing modules to identify and recognize the person with respect to the training data sets. The main aim of this project is to overcome the challenges faced in integrating the open source tools that build up the system for tagging and searching people.  

scholarly journals A versatile toolbox for semi-automatic cell-by-cell object-based colocalization analysis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Anders Lunde ◽  
Joel C. Glover
Keyword(s):  
3D Visualization ◽  
Image Data ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Cell Segmentation ◽  
Fluorescence Labeling ◽  
Data Sets ◽  
Object Based ◽  
Imagej Plugin

Abstract Differential fluorescence labeling and multi-fluorescence imaging followed by colocalization analysis is commonly used to investigate cellular heterogeneity in situ. This is particularly important when investigating the biology of tissues with diverse cell types. Object-based colocalization analysis (OBCA) tools can employ automatic approaches, which are sensitive to errors in cell segmentation, or manual approaches, which can be impractical and tedious. Here, we present a novel set of tools for OBCA using a semi-automatic approach, consisting of two ImageJ plugins, a Microsoft Excel macro, and a MATLAB script. One ImageJ plugin enables customizable processing of multichannel 3D images for enhanced visualization of features relevant to OBCA, and another enables semi-automatic colocalization quantification. The Excel macro and the MATLAB script enable data organization and 3D visualization of object data across image series. The tools are well suited for experiments involving complex and large image data sets, and can be used in combination or as individual components, allowing flexible, efficient and accurate OBCA. Here we demonstrate their utility in immunohistochemical analyses of the developing central nervous system, which is characterized by complexity in the number and distribution of cell types, and by high cell packing densities, which can both create challenging situations for OBCA.

Real-time volume splatter for large-scale data sets

2004 ◽  
Author(s):  
Jiawan Zhang ◽  
Jizhou Sun ◽  
Xiaotu Li ◽  
Mingchu Li ◽  
Xiaobing Sun ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Data Sets ◽  
Large Scale Data ◽  
Scale Data ◽  
Large Scale Data Sets

scholarly journals DESIGN AND IMPLEMENTATION OF A FOVEAL PROJECTION DISPLAY

2008 ◽  
Vol 08 (02) ◽  
pp. 243-263 ◽  
Author(s):  
BENJAMIN A. AHLBORN ◽  
OLIVER KREYLOS ◽  
SOHAIL SHAFII ◽  
BERND HAMANN ◽  
OLIVER G. STAADT
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Image Data ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Resolution Image ◽  
High Resolution Image ◽  
Projection Display ◽  
Display Resolution

We introduce a system that adds a foveal inset to large-scale projection displays. The effective resolution of the foveal inset projection is higher than the original display resolution, allowing the user to see more details and finer features in large data sets. The foveal inset is generated by projecting a high-resolution image onto a mirror mounted on a panCtilt unit that is controlled by the user with a laser pointer. Our implementation is based on Chromium and supports many OpenGL applications without modifications.We present experimental results using high-resolution image data from medical imaging and aerial photography.

scholarly journals Biological data annotation via a human-augmenting AI-based labeling system

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Douwe van der Wal ◽  
Iny Jhun ◽  
Israa Laklouk ◽  
Jeff Nirschl ◽  
Lara Richer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Microscopic Analysis ◽  
Image Data ◽  
Cell Types ◽  
Biological Data ◽  
Data Sets ◽  
Data Set ◽  
Data Annotation ◽  
Labeling System

AbstractBiology has become a prime area for the deployment of deep learning and artificial intelligence (AI), enabled largely by the massive data sets that the field can generate. Key to most AI tasks is the availability of a sufficiently large, labeled data set with which to train AI models. In the context of microscopy, it is easy to generate image data sets containing millions of cells and structures. However, it is challenging to obtain large-scale high-quality annotations for AI models. Here, we present HALS (Human-Augmenting Labeling System), a human-in-the-loop data labeling AI, which begins uninitialized and learns annotations from a human, in real-time. Using a multi-part AI composed of three deep learning models, HALS learns from just a few examples and immediately decreases the workload of the annotator, while increasing the quality of their annotations. Using a highly repetitive use-case—annotating cell types—and running experiments with seven pathologists—experts at the microscopic analysis of biological specimens—we demonstrate a manual work reduction of 90.60%, and an average data-quality boost of 4.34%, measured across four use-cases and two tissue stain types.

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