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MPI based Edge Detection of Coloured Image using Laplacian of Gaussian Filter

Published on October 2014 by Prakash K. Aithal, U. Dinesh Acharya, Rajesh G
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International Conference on Information and Communication Technologies
Foundation of Computer Science USA
ICICT - Number 2
October 2014
Authors: Prakash K. Aithal, U. Dinesh Acharya, Rajesh G
d95e42d3-1104-4971-9482-182dd88f078b

Prakash K. Aithal, U. Dinesh Acharya, Rajesh G . MPI based Edge Detection of Coloured Image using Laplacian of Gaussian Filter. International Conference on Information and Communication Technologies. ICICT, 2 (October 2014), 5-7.

@article{
author = { Prakash K. Aithal, U. Dinesh Acharya, Rajesh G },
title = { MPI based Edge Detection of Coloured Image using Laplacian of Gaussian Filter },
journal = { International Conference on Information and Communication Technologies },
issue_date = { October 2014 },
volume = { ICICT },
number = { 2 },
month = { October },
year = { 2014 },
issn = 0975-8887,
pages = { 5-7 },
numpages = 3,
url = { /proceedings/icict/number2/17966-1410/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Information and Communication Technologies
%A Prakash K. Aithal
%A U. Dinesh Acharya
%A Rajesh G
%T MPI based Edge Detection of Coloured Image using Laplacian of Gaussian Filter
%J International Conference on Information and Communication Technologies
%@ 0975-8887
%V ICICT
%N 2
%P 5-7
%D 2014
%I International Journal of Computer Applications
Abstract

Edge detection is the process of identifying points in a digital image at which image brightness changes sharply. Edge detection is one of the fundamental tasks in Image Processing. Image processing tasks such as object identification, segmentation and robot vision requires high quality edge detection. In this paper an edge detection using Message Passing Interface (MPI) has been implemented with 2, 4 and 8 processes. The paper compares the result with sequential implementation of edge detection. There is an improvement in performance as we increase the number of processes. Depending on the image size we can increase the number of processes. MPI is more suitable for a large size images like geospatial image. The edge detection using MPI is number of processes time faster than its sequential counterpart and speed up is approximately equal to number of processes used. The performance improved by a factor of 5 with 5 processes for colored image and by a factor of 4 for grey scaled image.

References
  1. ArpitaGopal, SonaliPatil, AmreshNikamInternational Journal of Computer Science and Application Issue 2010. A Parallel Algorithm for Image Edge Detection using difference chain encoding.
  2. Lei Zhai, Shouping Dong, Honglian Ma. 2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing. Recent Methods and Applications on Image Edge Detection
  3. Raman Maini, HimanshuAggarwalStudy and Comparison of Various Image Edge Detection Techniques. International Journal of Image Processing (IJIP), Volume (3) : Issue (1).
  4. Rajiv Chopra, Advanced computer architecture- A practical approach. S chand company ltd, first edition 2009.
  5. Image is taken from Sample Imagery List of GeoEye Inc. (formerly Orbital Imaging Corporation or ORBIMAGE) which is an American commercial satellite imagery company.
Index Terms

Computer Science
Information Sciences

Keywords

Edge Detection Mpi Laplacian Of Gaussian Filter.