scholarly journals A Hierarchical Distributed Processing Framework for Big Image Data

2016 ◽  
Vol 2 (4) ◽  
pp. 297-309 ◽  
Author(s):  
Le Dong ◽  
Zhiyu Lin ◽  
Yan Liang ◽  
Ling He ◽  
Ning Zhang ◽  
...  
Keyword(s):  
Distributed Processing ◽  
Image Data ◽  
Processing Framework

scholarly journals Labkit: Labeling and Segmentation Toolkit for Big Image Data

2021 ◽  
Author(s):  
Matthias Arzt ◽  
Joran Deschamps ◽  
Christopher Schmied ◽  
Tobias Pietzsch ◽  
Deborah Schmidt ◽  
...  
Keyword(s):  
Graphics Processing Units ◽  
High Performance ◽  
Distributed Processing ◽  
Image Data ◽  
Microscopy Image ◽  
Processing Step ◽  
Graphics Processing ◽  
User Friendly ◽  
Automated Image Processing ◽  
Performance Computing

We present Labkit, a user-friendly Fiji plugin for the segmentation of microscopy image data. It offers easy to use manual and automated image segmentation routines that can be rapidly applied to single- and multi-channel images as well as to timelapse movies in 2D or 3D. Labkit is specifically designed to work efficiently on big image data and enables users of consumer laptops to conveniently work with multiple-terabyte images. This efficiency is achieved by using ImgLib2 and BigDataViewer as the foundation of our software. Furthermore, memory efficient and fast random forest based pixel classification inspired by the Waikato Environment for Knowledge Analysis (Weka) is implemented. Optionally we harness the power of graphics processing units (GPU) to gain additional runtime performance. Labkit is easy to install on virtually all laptops and workstations. Additionally, Labkit is compatible with high performance computing (HPC) clusters for distributed processing of big image data. The ability to use pixel classifiers trained in Labkit via the ImageJ macro language enables our users to integrate this functionality as a processing step in automated image processing workflows. Last but not least, Labkit comes with rich online resources such as tutorials and examples that will help users to familiarize themselves with available features and how to best use \Labkit in a number of practical real-world use-cases.

scholarly journals Support for "cub" image format

2006 ◽  
Author(s):  
Pablo Burstein ◽  
Paul Yushkevich ◽  
James Gee
Keyword(s):  
Functional Mri ◽  
Functional Neuroimaging ◽  
Distributed Processing ◽  
3D Image ◽  
Subsequent Work ◽  
Anatomical Data ◽  
The University ◽  
Image Format ◽  
Processing Framework

VoxBo (www.voxbo.org) is a distributed processing framework, developed at the Center of Functional Neuroimaging at the University of Penssylvania, with the purpose of processing and analyzing brain functional MRI data. VoxBo is written in C++, and it is escentially a fair translation of some SPM functionality, originally written in Matlab. Along with the software, VoxBo defines two new image formats: 1) .cub, a 3D image format, used for anatomical data and single EPI frames. It is important to notice that this format defines a proper origin field, lacking, for instance, in Analyze. 2) .tes, a 4D image format, designed to contain the temporal EPI information. It is worth noticing that this format organizes the voxels in such a way to optimize longitudinal (temporal) voxel access. Furthermore, the tes format is compressed through encoding of the functional voxels alone, i.e., background voxels are not stored for each frame, but only for the first one.In this work we present the necessary IO classes that allow ImageFileReader and ImageFileWriter to properly identify, read and write .cub files only. We will provide .tes support in a subsequent work. This effort is carried out with the purpose of letting VoxBo implementors to take advantage of ITK’s power and overcome the limitations currently afflicting VoxBo.

Introduction

2019 ◽  
pp. 1-6
Author(s):  
Matteo Colombo ◽  
Liz Irvine ◽  
Mog Stapleton
Keyword(s):  
Distributed Processing ◽  
Extended Mind ◽  
Predictive Processing ◽  
Andy Clark ◽  
Cognitive Scientist ◽  
Mind And Brain ◽  
Opening Up ◽  
Intense Debate ◽  
Processing Framework

Andy Clark is a leading philosopher and cognitive scientist. His work has been wide-ranging and inspiring. The extended mind hypothesis, the power of parallel distributed processing, the role of language in opening up novel paths for thinking, the flexible interface between biological minds and artificial technologies, the significance of representation in explanations of intelligent behaviour, the promise of the predictive processing framework to unify the cognitive sciences: these are just some of the ideas illuminated by Clark’s work that have sparked intense debate across the sciences of mind and brain. This introduction puts into focus some of the major motifs running through Clark’s work and outlines the content and structure of the volume.

A Distributed Processing Framework of Incremental Text Clustering under the Background of Big Data

2014 ◽  
Vol 1049-1050 ◽  
pp. 1421-1426
Author(s):  
Zhen Tan ◽  
Yi Fan Chen ◽  
Zhong Lin Shi ◽  
Bin Ge ◽  
Yan Li Hu ◽  
...  
Keyword(s):  
Big Data ◽  
Clustering Algorithm ◽  
Distributed Processing ◽  
Text Clustering ◽  
Rapid Expansion ◽  
Distributed Model ◽  
Sharp Decline ◽  
Model Framework ◽  
Data Volume ◽  
Processing Framework

In the era of big data, due to the rapid expansion of the data, the existing incremental text clustering algorithm has the drawback that the efficiency of algorithm will sharp decline with the time and data volume increasing. Because of poor timeliness and robustness, the algorithms are hard to be applied in practice. In this paper, we propose a distributed model framework of Single-Pass algorithm based on MapReduce, the experiments result of increment text cluster is accuracy, the algorithm effectively improve the computing efficiency of the algorithm and real-time of result. Algorithm has a great prospect under the background of big data.

INTELLIGENT VISION SYSTEMS FOR ROBOTIC APPLICATIONS

2008 ◽  
Vol 05 (03) ◽  
pp. 259-267 ◽  
Author(s):  
ANDREAS MAEDER ◽  
HANNES BISTRY ◽  
JIANWEI ZHANG
Keyword(s):  
Distributed Processing ◽  
Image Data ◽  
Vision Systems ◽  
Processing Power ◽  
Robot Systems ◽  
Processing Resources ◽  
Single Sensor ◽  
Natural Solution ◽  
Realtime Control ◽  
Robotic Applications

Vision-based sensors are a key component for robot-systems, where many tasks depend on image data. Realtime control constraints bind a lot of processing power for only a single sensor modality. Dedicated and distributed processing resources are the "natural" solution to overcome this limitation. This paper presents experiments, using embedded processors as well as dedicated hardware, to execute various image (pre)processing tasks. Architectural concepts and requirements for intelligent vision systems have been acquired.

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