Massive Image Treatment System Based on Cloud Computing Platform

2014 ◽  
Vol 687-691 ◽  
pp. 3733-3737
Author(s):  
Dan Wu ◽  
Ming Quan Zhou ◽  
Rong Fang Bie
Keyword(s):  
Image Processing ◽  
Cloud Computing ◽  
High Performance ◽  
Large Scale ◽  
Processing System ◽  
Virtual Space ◽  
Image Processing System ◽  
Computing Platform ◽  
Simulation Calculation ◽  
Computer Resources

Massive image processing technology requires high requirements of processor and memory, and it needs to adopt high performance of processor and the large capacity memory. While the single or single core processing and traditional memory can’t satisfy the need of image processing. This paper introduces the cloud computing function into the massive image processing system. Through the cloud computing function it expands the virtual space of the system, saves computer resources and improves the efficiency of image processing. The system processor uses multi-core DSP parallel processor, and develops visualization parameter setting window and output results using VC software settings. Through simulation calculation we get the image processing speed curve and the system image adaptive curve. It provides the technical reference for the design of large-scale image processing system.

scholarly journals Cloud Computing Cloud Computing in Remote Sensing : High Performance Remote Sensing Data Processing in a Big data Environment

2021 ◽  
Vol 16 (6) ◽  
Author(s):  
Yassine Sabri ◽  
Aouad Siham
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Data Processing ◽  
High Performance ◽  
Large Scale ◽  
Processing System ◽  
Remote Sensing Data ◽  
Cloud Service ◽  
Intermediate Data ◽  
High Level

Multi-area and multi-faceted remote sensing (SAR) datasets are widely used due to the increasing demand for accurate and up-to-date information on resources and the environment for regional and global monitoring. In general, the processing of RS data involves a complex multi-step processing sequence that includes several independent processing steps depending on the type of RS application. The processing of RS data for regional disaster and environmental monitoring is recognized as computationally and data demanding.Recently, by combining cloud computing and HPC technology, we propose a method to efficiently solve these problems by searching for a large-scale RS data processing system suitable for various applications. Real-time on-demand service. The ubiquitous, elastic, and high-level transparency of the cloud computing model makes it possible to run massive RS data management and data processing monitoring dynamic environments in any cloud. via the web interface. Hilbert-based data indexing methods are used to optimally query and access RS images, RS data products, and intermediate data. The core of the cloud service provides a parallel file system of large RS data and an interface for accessing RS data from time to time to improve localization of the data. It collects data and optimizes I/O performance. Our experimental analysis demonstrated the effectiveness of our method platform.

High-performance image processing system

1994 ◽  
Author(s):  
Daniel F. McCarthy ◽  
Michael S. Patterson ◽  
Michael Younger ◽  
Clyde C. DeLuca
Keyword(s):  
Image Processing ◽  
High Performance ◽  
Processing System ◽  
Image Processing System

FPGA + DSP-Based Real-Time Image Acquisition System Research and Design

2012 ◽  
Vol 433-440 ◽  
pp. 5482-5488 ◽  
Author(s):  
Su Ran Kong
Keyword(s):  
Image Processing ◽  
Real Time ◽  
High Performance ◽  
Image Acquisition ◽  
Processing System ◽  
Acquisition System ◽  
Image Processing System ◽  
Real Time Image ◽  
Image Acquisition System ◽  
Time Image

Image processing system to calculate the volume, real-time high and the requirements of small size, using the DSP-based processor, FPGA approach, supplemented by the processor design of a high-performance real-time image processing system, and the system In the process of image acquisition and transmission of noise, using the PCB's anti-jamming design. Practice shows that two chips using FPGA + DSP, the algorithm is divided into two parts by the FPGA and DSP processing; effectively improve the efficiency of the algorithm. System real-time high, adaptability, real-time image acquisition system can meet the design requirements.

The Weld Image Acquisition and Preprocessing Based on FPGA

2013 ◽  
Vol 401-403 ◽  
pp. 1507-1513 ◽  
Author(s):  
Zhong Hu Yuan ◽  
Wen Tao Liu ◽  
Xiao Wei Han
Keyword(s):  
Image Processing ◽  
Real Time ◽  
High Speed ◽  
High Performance ◽  
Image Acquisition ◽  
Processing System ◽  
Large Data ◽  
Seam Tracking ◽  
Image Processing System ◽  
Time Requirements

In the weld image acquisition system, real-time image processing has been a difficult design bottleneck to break through, especially for the occasion of large data processing capability and more demanding real-time requirements, in which the traditional MCU can not adapt, so using high-performance FPGA as the core of the high speed image acquisition and processing card, better meets the large amount of data in most of the image processing system and high demanding real-time requirements. At the same time, system data collection, storage and display were implemented by using Verilog, and in order to reducing the influence of edge detection noise, the combination of image enhancement and median filtering image preprocessing algorithm was used. Compared to the pre-processing algorithm of the software implementation, it has a great speed advantage, and simplifies the subsequent processing work load, improves the speed and efficiency of the entire image processing system greatly. So it proves that the system has strong ability of restraining the noise of image, and more accurate extracted edge positioning, it can be applied in the seam tracking field which need higher real-time requirements.

scholarly journals Mangrove Vegetation Mapping Using Sentinel-2A Imagery Based on Google Earth Engine Cloud Computing Platform

2021 ◽  
Vol 6 (1) ◽  
pp. 249-255
Author(s):  
Luhur Moekti Prayogo
Keyword(s):  
Image Processing ◽  
Cloud Computing ◽  
High Performance ◽  
Google Earth ◽  
Mangrove Forests ◽  
Application Development ◽  
Computing Platform ◽  
Mapping Technology ◽  
Google Earth Engine ◽  
Sentinel 2A

Mangroves are trees whose habitat is affected by tides, and their presence has decreased from year to year. Today, mapping technology has undergone many developments, including the availability of images of various resolutions and cloud-based image processing. One of the popular platforms today is the Google Earth Engine. Google Earth Engine is a cloud-based platform that makes it easy to access high-performance computing resources for extensive processing. The advantage of using Google Earth Engine is that users do not have to be IT experts without experts in application development, WEB programming, and HTML. This study aims to conduct a study on mangrove mapping in Gili Genting District with Sentinel-2A imagery using a Google Earth Engine. This location was chosen since there are still many mangroves, especially on the Gili Raja and Gili Genting Islands. From this research, it can be concluded that cloud computing-based Sentinel-2A image processing shows that the vegetation value of NDVI results ranges from -0.923208 to 0.75579. The classification results show that mangrove forests' overall presence on Gili Genting Island is more expansive than Gili Raja Island with 16.74 ha and 14.75 ha. The use of the Google Earth Engine platform simplifies the analysis process because image processing can be done once with various scripts so that analysis becomes faster.

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