Canny Edge Detector Algorithm Optimization Using 2D Spatial Separable Convolution

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.

Keywords

• canny edge detector
• gaussian filter
• discrete convolution
• separable convolution
• kernel matrix