Extracting an explicitly data-parallel representation of image-processing programs

2004 ◽  
Author(s):  
L. Baumstark ◽  
M. Guler ◽  
L. Wills
Keyword(s):  
Image Processing ◽  
Data Parallel

A Data Parallel Implementation Scheme of Geometric Operations

2014 ◽  
Vol 519-520 ◽  
pp. 719-723
Author(s):  
Guang Wang
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Digital Image Processing ◽  
Digital Image ◽  
Parallel Implementation ◽  
Computation Complexity ◽  
The Real ◽  
Data Parallel ◽  
Implementation Scheme ◽  
Time Requirements

A data parallel implementation of geometric operations is proposed and conclusions are proved. It shows that the computation complexity of data parallel implementation scheme presented in this paper is Ο(M+N). It can be used to improve the efficiency of geometric operations and can easily meet the real time requirements of the digital image processing.

Parallel image processing with the block data parallel architecture

1996 ◽  
Vol 84 (7) ◽  
pp. 947-968 ◽  
Author(s):  
W.E. Alexander ◽  
D.S. Reeves ◽  
C.S. Gloster
Keyword(s):  
Image Processing ◽  
Parallel Architecture ◽  
Data Parallel ◽  
Parallel Image Processing ◽  
Parallel Image ◽  
Block Data

Data Parallel Implementation of Geometric Operations

2014 ◽  
Vol 889-890 ◽  
pp. 875-880
Author(s):  
Guang Wang
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Parallel Implementation ◽  
Zero Order ◽  
First Order ◽  
Sequential Processing ◽  
Data Parallel ◽  
Implementation Scheme ◽  
Time Requirements ◽  
Backward Mapping

The data parallel implementation scheme of zero-order interpolation and first-order interpolation of backward mapping is proved and discussed. It is shown that the complexity of data parallel implementation scheme presented in this paper is Ο(M+N) instead of Ο(MN) in sequential processing, thereby it can easily meet the real time requirements of the digital image processing.

scholarly journals Hilbert space filling curve using scilab

2018 ◽  
Vol 7 (1.9) ◽  
pp. 129
Author(s):  
Sushma T.V ◽  
Roopa M
Keyword(s):  
Image Processing ◽  
Hilbert Space ◽  
Image Compression ◽  
Dimensional Space ◽  
Linear Mapping ◽  
Space Filling ◽  
Space Filling Curve ◽  
Data Parallel ◽  
Locality Preserving ◽  
Filling Curve

Space filling curve is used widely for linear mapping of multi-dimensional space. This provides a new line of thinking for various applications in image processing, Image compression being the most widely used. The paper highlights the locality preserving property of Hilbert Space filling curve which is essential in numerous applications such asin image compression, numerical analysis of a large aray of data, parallel processing and so on. A simplistic approach forusingHilbert Space filling curve using Scilab code has been presented.

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