<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In the case of real-time image processing, it is necessary to determine the computational complexity of the mathematical operations used. Reduction of computational complexity of 2D discrete convolution can be achieved by using a separable convolution. In this article, we focus on the application of a canny edge detector for different types of images. The main goal was to speed up the process of applying the kernel matrix to a given image using a separable convolution. By applying a separable convolution, we compared the duration of the Gaussian filter application, edges detection and the Hysteresis threshold level. Applying a separable convolution should speed up the duration of the 2D Gaussian filter as well as the edge detection. The main variable that interested us was time, but an important factor in the application of the filter and edge detection is the number of operating cycles. The use of a separable convolution should significantly reduce the number of computational cycles and reduces the duration of filter application and detection.</p>
</abstract>

In the case of real-time image processing, it is necessary to determine the computational complexity of the mathematical operations used. Reduction of computational complexity of 2D discrete convolution can be achieved by using a separable convolution. In this article, we focus on the application of a canny edge detector for different types of images. The main goal was to speed up the process of applying the kernel matrix to a given image using a separable convolution. By applying a separable convolution, we compared the duration of the Gaussian filter application, edges detection and the Hysteresis threshold level. Applying a separable convolution should speed up the duration of the 2D Gaussian filter as well as the edge detection. The main variable that interested us was time, but an important factor in the application of the filter and edge detection is the number of operating cycles. The use of a separable convolution should significantly reduce the number of computational cycles and reduces the duration of filter application and detection.

Canny Edge Detector Algorithm Optimization Using 2D Spatial Separable Convolution

Acta Electrotechnica et Informatica

This work is licensed under the Creative Commons Attribution 4.0 International License.

In the case of real-time image processing, it is necessary to determine the computational complexity of the mathematical operations used. Reduction of...

Canny Edge Detector Algorithm Optimization Using 2D Spatial Separable Convolution

Published Online: Apr 13, 2022

Page range: 36 - 43

Received: Nov 24, 2021

Accepted: Dec 16, 2021

DOI: https://doi.org/10.2478/aei-2021-0006

Keywords
Canny edge detector, Gaussian filter, discrete convolution, separable convolution, kernel matrix

© 2021 Martin Králik et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Canny Edge Detector Algorithm Optimization Using 2D Spatial Separable Convolution

Published Online: Apr 13, 2022

Page range: 36 - 43

Received: Nov 24, 2021

Accepted: Dec 16, 2021

DOI: https://doi.org/10.2478/aei-2021-0006

KeywordsCanny edge detector, Gaussian filter, discrete convolution, separable convolution, kernel matrix

© 2021 Martin Králik et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Canny edge detector, Gaussian filter, discrete convolution, separable convolution, kernel matrix