Components and workflow based Grid programming environment for integrated image-processing applications

2006 ◽  
Vol 18 (14) ◽  
pp. 1857-1869 ◽  
Author(s):  
Hai Jin ◽  
Ran Zheng ◽  
Qin Zhang ◽  
Ying Li
Keyword(s):  
Image Processing ◽  
Programming Environment ◽  
Grid Programming

WSPE: a peer-to-peer grid programming environment

2009 ◽  
Vol 21 (13) ◽  
pp. 1709-1724 ◽  
Author(s):  
Romulo B. Rosinha ◽  
Cláudio F. R. Geyer ◽  
Patrícia Kayser Vargas
Keyword(s):  
Peer To Peer ◽  
Programming Environment ◽  
Grid Programming

OpenGR: A directive-based grid programming environment

2005 ◽  
Vol 31 (10-12) ◽  
pp. 1140-1154
Author(s):  
Motonori Hirano ◽  
Mitsuhisa Sato ◽  
Yoshio Tanaka
Keyword(s):  
Programming Environment ◽  
Grid Programming

scholarly journals Ibis: a flexible and efficient Java-based Grid programming environment

2005 ◽  
Vol 17 (7-8) ◽  
pp. 1079-1107 ◽  
Author(s):  
Rob V. van Nieuwpoort ◽  
Jason Maassen ◽  
Gosia Wrzesi?ska ◽  
Rutger F. H. Hofman ◽  
Ceriel J. H. Jacobs ◽  
...  
Keyword(s):  
Programming Environment ◽  
Grid Programming

The Study of Automatic Counting of Coal and Gangue Based on the DaVinci Video Image Processing Technology

2011 ◽  
Vol 340 ◽  
pp. 95-98
Author(s):  
Yi Ding Zhao ◽  
Ming Feng Sun
Keyword(s):  
Image Processing ◽  
High Performance ◽  
Ccd Camera ◽  
Recognition System ◽  
Programming Environment ◽  
Auxiliary Equipment ◽  
Automatic Counting ◽  
Real Time Image ◽  
Time Image ◽  
Video Image Processing

In this paper, an image processing and recognition system of the coal and gangue has been studied, which is made up by the chip DM6437 and some peripheral auxiliary equipment. The image processing arithmetic is realized by the CCS programming environment, and loaded into the target board through the simulator XDS510. The system takes advantage of the high performance of the chip to process the real-time image collected by the CCD camera, and makes count of coal and gangue. In this paper, we mainly use the coal and gangue’s trend of contours to make count，and we achieved to make count of the continuous and irregular coal and gangue in the rapid movement of the belt.

scholarly journals Leaf Disease Detection using Labview Imaq Vision

2020 ◽  
Vol 9 (3) ◽  
pp. 481-492
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Detection Algorithm ◽  
Agricultural Products ◽  
Programming Environment ◽  
Graphical Programming ◽  
Sample Data ◽  
Plane Extraction ◽  
The Look ◽  
Programming Algorithms

The intension of our project is to design a system which can identify the good leaves from the diseased ones. Image processing is a powerful tool capable of many applications. Image processing combined with Machine Vision can simulate and execute real time projects. In this project we have used LabVIEW along with IMAQ Vision to acquire real time images and process them. LabVIEW IMAQ Vision is potentially useful for agricultural products since it combines the merits of both LabVIEW and IMAQ Vision, which have graphical programming environment and rich image processing functions. The project aims to provide a brief introduction into the IMAQ vision components like Image Acquisition, Calibration, Defect detection. Major leaf diseases’ symptoms include spots or discolouration of leaves. The presence or absence of macro and micro nutrients, bug infestation and other diseases can be identified through leaves. In this project we have obtained the images through LabVIEW IMAQ vision pallet. Further on two procedures were followed – one based on colour of the leaves and other is based on spots and patterns present on the leaves. For the discolouration we first split the image into its constituent planes- RGB and CMYK, here we used Green, Cyan and Yellow planes. Then on we decided a threshold based on sample data using Linear Regression based prediction model of Machine Learning to classify the data into three states – safe, risk and high risk.The second method was detecting spots. First, we split the images into its constituent planes to convert the RGB image to Greyscale and increase the contrast using the Colour Plane Extraction tool then use the Look up table tool to further enhance the contrast. Then on locate the bright objects and then using dilation from the Morphology tool box we increase the size of the spots to increase detection rate. Using Advanced Morphology tool box we removed the boundary objects to isolate the spots. Then using the shape detection or circle detection algorithm we can detect the spots. Several samples were obtained and are successfully classified. Finally, current limitations and likely future development trends are discussed. Combining LabVIEW along with different programming algorithms can help in raising the accuracy of the system.

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