## 5 Results and Discussion  
Figure 2 displays the original image (No. 61060) from BSD500 dataset [32; 33] (license: Not found) and the intermediate output images of the spiking detector network after the Gaussian kernel, gradient calculation, non-maximum suppression, double-threshold filtering, and edge-tracking by hysteresis. The intermediate features show the spike domain implementation is effective in performing all five steps involved in the conventional Canny edge detection algorithm. The edge map resulting from the spike-based computation, i.e., the output of the network after the edge-tracking hysteresis step, can be referred to as the neuromorphic Canny edge detector or SNN-based Canny edge detector. We find that the resolution of the images from BSD500 dataset is sufficient to evaluate the qualitative and quantitative performance of edge detection. In Figure 3, we compare the neuromorphic Canny detector with the Sobel detector and the conventional Canny detector for a set of four images and their ground truth edges from the BSD500 dataset [32; 33]. We find the neuromorphic Canny detector detects edges similarly to the edges generated by both the conventional detectors. The spike-based detection reported here results in relatively more highlighted edges in the objects while retaining some of the structural features that seem to have been lost in the conventional edge detectors. This can be attributed to the difference in the range of thresholds used in the spike domain implementation compared to the conventional Canny edge detector. With an implementation on a low size, weight and power (SWaP) hardware, for example, by emulating spikes on a field programmable gate array (FPGA) [34] or edge computing devices, such as NVIDIA Jetson TX2, together with a neuromorphic camera, the spike domain image processing algorithm will be attractive for new applications, for example, in handheld computer vision systems and for low-Earth satellite-based object detection. Figure 2: The original and the output images from all 5 stages of neuromorphic Canny edge detection. In image processing, it is common to select a few images and demonstrate how the method works. Statistics on training sets and test sets usually applies to machine learning experiments, which is the not the case in the present study. All the images that are pictures of natural scenes with edges and none of the examples are synthesized. From visual inspection, the SNN edge detector appears to render wider edges and detect more background information. It is possible that the difference in the SNN Canny edge map compared to the conventional Canny edge map is also related to the choice of the threshold used or the smoothing parameters. The SNN edge maps are realized in a narrower threshold range, and this makes it difficult to perform an ablation study, which is typically done in machine learning experiments. We would like to note that the primary goal of the present work is to demonstrate a spike-based implementation of Canny edge detection, not the superior computational efficiency. Currently, there is no hardware that can implement the exact version of the neuron model used in the present work to evaluate the computational time for the targeted neuromorphic domain. The context of this work is that it addresses the question of whether it is possible to compute the steps of the Canny edge algorithm by exclusively using spikes. The first step of smoothing with the Gaussian kernel is performed by using the HH neurons. The rest of the steps involve the algebraic computations in the spike domain without introducing the neurons to match the method used for the conventional Canny edge detection algorithm. We do not claim that the spike-based algorithm can be faster. The research is conducted to test if Canny-type edge maps can be generated if the data are collected with an event camera rather than using an image taken by a conventional camera.