A Review of Parallel Edge Detection in Grid Computing System

DOI

10.2991/978-94-6239-789-7_20How to use a DOI?

Keywords

Edge detection; Sobel operator; Canny edge detector; Laplacian of Gaussian; gradient-based methods; second-order derivative operators; digital image processing; FPGA

Abstract

Edge detection is a fundamental task in digital image processing, enabling the identification of object boundaries and structural features required for higher-level vision applications. Classical edge detection techniques, including Sobel, Canny, and Laplacian-based methods, remain widely adopted due to their deterministic behavior, low complexity, and broad applicability. This survey presents a consolidated review of classical edge detection algorithms, covering their theoretical foundations, algorithmic variations, and application-oriented studies reported in the literature. Reported results indicate that Sobel-based approaches typically achieve peak signal-to-noise ratio (PSNR) values in the range of 19–25 dB for grayscale and medical images, while Canny-based methods demonstrate higher detection accuracy, commonly between 88% and 95%, particularly under noisy conditions. Laplacian and Laplacian of Gaussian techniques provide accurate edge localization through zero-crossing analysis, with sensitivity values generally reported in the range of 80–90% for biomedical and agricultural applications. The survey highlights key trade-offs among robustness, accuracy, and computational cost, offering guidance for selecting appropriate edge detection methods based on application requirements.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Anjali Thakur
AU  - Seema Rani
AU  - Anu Saini
AU  - Himanshu Sharma
AU  - Asha Bhatt
AU  - Sonia Moudgil
PY  - 2026
DA  - 2026/10/08
TI  - A Review of Parallel Edge Detection in Grid Computing System
BT  - Proceedings of theInternational Conference on Innovation and Practices in Artificial Intelligence (ICIPAI 2026)
PB  - Atlantis Press
SP  - 281
EP  - 295
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-789-7_20
DO  - 10.2991/978-94-6239-789-7_20
ID  - Thakur2026
ER  -