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Comparative Analysis of Efficient Platforms: Scalable Algorithms and Parallel Paradigms for Large Scale Image Processing
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Author(s): Khawaja Tehseen Ahmed (Bahauddin Zakariya Univeristy, Pakistan), Mazhar Ul-Haq (NUST School of Electrical Engineering and Computer Science, Pakistan), Arsalaan Ahmed Shaikh (NUST School of Electrical Engineering and Computer Science, Pakistan)and Raihan ur Rasool (NUST School of Electrical Engineering and Computer Science, Pakistan)
Copyright: 2017
Pages: 20
Source title:
Biometrics: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-0983-7.ch048
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Abstract
With the advancement of technology we are heading towards a paperless environment. But there are still a large numbers of documents that exist in paper format in our daily lives. Thus the need to digitize these paper documents, archive them and view them at all times has arisen. The number of documents of a small organization may be in thousands, millions or even more. This chapter presents comparative analysis of different programming languages and libraries where it is intended to parallel process a huge stream of images which undergo unpredictable arrival of the images and variation in time. Since the parallelism can be implemented at different levels, different algorithms and techniques have also been discussed. It also presents the state of the art and discussion of various existing technical solutions to implement the parallelization on a hybrid platform for the real time processing of the images contained in a stream. Experimental results obtained using Apache Hadoop in combination with OpenMP have also been discussed.
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