z-logo
open-access-imgOpen Access
Development of Edge Detection for Image Segmentation
Author(s) -
Zaheera Zainal Abidin,
Siti Azirah Asmai,
Zuraida Abal Abas,
Nor Aini Zakaria,
Soad Ibrahim
Publication year - 2020
Publication title -
iop conference series materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/864/1/012058
Subject(s) - prewitt operator , artificial intelligence , image gradient , canny edge detector , edge detection , sobel operator , computer vision , image segmentation , computer science , mathematics , pattern recognition (psychology) , image texture , segmentation , image (mathematics) , image processing
The edge detection technique is a fundamental phase of image segmentation. The purpose of the image segmentation algorithm is to distinguish the boundary of objects in different regions and it relies on discontinuities in image values between distinct regions. The objectives of this research are to a) develop an interface for image edge detection based on derivatives using MATLAB and b) measure the PSNR, SNR and MSE values for analysis based on experiments conducted. Results show that, Lena image produces PSNR values of 20.9 dB (Canny), 20.0 dB (Log), 20.1 dB (Prewitt), 20.0 dB (Sobel) and 20.0 dB (Robert). Meanwhile, MSE gives 80.5 dB (Canny), 83.1 dB (Log), 80.9 dB (Prewitt), 81.0 dB (Sobel) and 81.0 dB (Robert) after the edge detection process. The finding shows that Canny has given a winning performance in PSNR value and low in noise rate for JPEG type of image in image segmentation. Finally, the impact of edge detection techniques produces a better solution for image segmentation.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom