## 2 Background  
An edge detection algorithm is widely used in computer vision to locate object boundaries in images. An edge in an image shows a sharp change in image brightness, which is a result of a sharp change in pixel intensity data. An edge detector computes and identifies the pixels with sharp changes in intensity with respect to the intensity of neighboring pixels. There are several edge detection image processing algorithms. The three stages in edge detection are image smoothing, detection, edge localization. There are mainly three types of operators in edge detection. These are (i) gradient-based, (ii) Laplacian-based and (iii) Gaussian-based. The gradient-based edge detection method detects the edges by finding the maximum and the minimum in the first derivative of the image using a threshold. The [PERSON] edge detector [5], Sobel edge detector [6], and Prewitt edge detector [7] are some of the examples of gradient-based edge detectors. These detectors use a 3 \(\times\) 3 pattern grid. A detailed discussion on these edge detectors and a comparison of their advantages and disadvantages can be found in [8]. The [PERSON] edge detection method is built on the idea that a difference on any pair of mutually perpendicular directions can be used to calculate the gradient. The Sobel operator uses the convolution of the images with a small, separable, and integer-valued filter in horizontal and vertical directions for edge detection. The Prewitt edge detector uses two masks, each computing the derivate of the image in the x-direction and the y-direction. This detector is suitable to estimate the magnitude and orientation of the edge. Laplacian-based edge detectors find the edges by searching for zero crossings in the second derivative of the image. The Laplacian of the Gaussian algorithm uses a pre-smoothing step with a Gaussian low-pass filter on an image followed by a second-order differential, i.e., Laplacian, which finds the image edge. This method needs a discrete convolutional kernel that can approximate the second derivative for the image which consists of discrete pixels. The Marr-Hildreth edge detector is also based on the Laplacian of the Gaussian operator [9]. The Gabor filter edge detector [10] and Canny edge detector [11] are Gaussian-based edge detectors. The Gabor filter is a linear filter with its impulse response function defined by the product of a harmonic function with a Gaussian function and is similar to the human perception system. The Canny edge detector provides excellent edge detection, as it meets the three criteria for edge detection [12]: (i) detection with low error rate, (ii) the edge point should localize in the center of the edge, and (iii) an edge should only be marked once and image noise should not create edges. Canny edge detection uses the calculus of variations to optimize a functional which is a sum of four exponential terms, which approximates the first derivative of a Gaussian. A Canny edge detector is a multi-step algorithm designed to detect the edges of any analyzed image. The steps of this process are: (1) removal of noise in the image using a Gaussian filter, (2) calculation of the gradient of the image pixels along x- and y-directions, (3) non-maximum suppression to thin out edges, (4) double-threshold filtering to detect strong, weak and non-relevant pixels, and (5) edge tracking by hysteresis to transform weaker pixels into stronger pixels if at least one of their neighbors is a stronger pixel. The Canny edge detection algorithm is highly cited (\(\sim\)36,000 citations) and the most commonly used edge detection algorithm [11]. Edge detection is a primary step in identifying an object and further research is strongly desirable to expand these methods to event-domain applications. The Canny edge detector has a better performance as an edge detector compared to [PERSON], [PERSON] and Prewitt edge detectors, but at a higher computational cost [8].