Parallelization of the local threshold and boolean function based edge detection algorithm using cuda

Raka Jovanovic; Milan Tuba; Dana Simian

Parallelization of the local threshold and boolean function based edge detection algorithm using cuda

Raka Jovanovic, Milan Tuba, Dana Simian

QEERI - Energy Center

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this paper we present a parallelized algorithm for edge detection for gray scale images. The chosen method is the local threshold and boolean function based edge detection. This method differs from common edge detectors in the use of bit map patterns instead of analyzing gradient changes in the image for edge recognition. The parallelization is implemented on the GPU, exploiting its multithreaded, many-core processor power using NVIDIA’s CUDA (Compute Unified Device Architecture). We show in our tests the significant speedup of parallelized algorithm compared to the sequential one.

Original language	English
Title of host publication	BICA'12: Proceedings of the 5th WSEAS congress on Applied Computing conference, and Proceedings of the 1st international conference on Biologically Inspired Computation
Publication status	Published - 2012

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Jovanovic, R., Tuba, M., & Simian, D. (2012). Parallelization of the local threshold and boolean function based edge detection algorithm using cuda. In BICA'12: Proceedings of the 5th WSEAS congress on Applied Computing conference, and Proceedings of the 1st international conference on Biologically Inspired Computation https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wpDJ5t4AAAAJ&pagesize=100&citation_for_view=wpDJ5t4AAAAJ:kNdYIx-mwKoC

@inproceedings{c417c684450e4fd385947046e4d4022d,

title = "Parallelization of the local threshold and boolean function based edge detection algorithm using cuda",

abstract = "In this paper we present a parallelized algorithm for edge detection for gray scale images. The chosen method is the local threshold and boolean function based edge detection. This method differs from common edge detectors in the use of bit map patterns instead of analyzing gradient changes in the image for edge recognition. The parallelization is implemented on the GPU, exploiting its multithreaded, many-core processor power using NVIDIA{\textquoteright}s CUDA (Compute Unified Device Architecture). We show in our tests the significant speedup of parallelized algorithm compared to the sequential one.",

author = "Raka Jovanovic and Milan Tuba and Dana Simian",

year = "2012",

language = "English",

booktitle = "BICA'12: Proceedings of the 5th WSEAS congress on Applied Computing conference, and Proceedings of the 1st international conference on Biologically Inspired Computation",

}

Jovanovic, R, Tuba, M & Simian, D 2012, Parallelization of the local threshold and boolean function based edge detection algorithm using cuda. in BICA'12: Proceedings of the 5th WSEAS congress on Applied Computing conference, and Proceedings of the 1st international conference on Biologically Inspired Computation. <https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wpDJ5t4AAAAJ&pagesize=100&citation_for_view=wpDJ5t4AAAAJ:kNdYIx-mwKoC>

Parallelization of the local threshold and boolean function based edge detection algorithm using cuda. / Jovanovic, Raka; Tuba, Milan; Simian, Dana.
BICA'12: Proceedings of the 5th WSEAS congress on Applied Computing conference, and Proceedings of the 1st international conference on Biologically Inspired Computation. 2012.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Parallelization of the local threshold and boolean function based edge detection algorithm using cuda

AU - Jovanovic, Raka

AU - Tuba, Milan

AU - Simian, Dana

PY - 2012

Y1 - 2012

N2 - In this paper we present a parallelized algorithm for edge detection for gray scale images. The chosen method is the local threshold and boolean function based edge detection. This method differs from common edge detectors in the use of bit map patterns instead of analyzing gradient changes in the image for edge recognition. The parallelization is implemented on the GPU, exploiting its multithreaded, many-core processor power using NVIDIA’s CUDA (Compute Unified Device Architecture). We show in our tests the significant speedup of parallelized algorithm compared to the sequential one.

AB - In this paper we present a parallelized algorithm for edge detection for gray scale images. The chosen method is the local threshold and boolean function based edge detection. This method differs from common edge detectors in the use of bit map patterns instead of analyzing gradient changes in the image for edge recognition. The parallelization is implemented on the GPU, exploiting its multithreaded, many-core processor power using NVIDIA’s CUDA (Compute Unified Device Architecture). We show in our tests the significant speedup of parallelized algorithm compared to the sequential one.

M3 - Conference contribution

BT - BICA'12: Proceedings of the 5th WSEAS congress on Applied Computing conference, and Proceedings of the 1st international conference on Biologically Inspired Computation

ER -

Parallelization of the local threshold and boolean function based edge detection algorithm using cuda

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