# Dilated Filters for Edge-Detection Algorithms  
[PERSON]  
1 Department of Communications, Politechnica University of Timisoara, 2, Piata Victorieie, 300006 Timisoara, Romania; [EMAIL_ADDRESS] (C.O. ); [EMAIL_ADDRESS] (R.V.) [PERSON]  
2 The eAustria Research Institute, West University of Timisoara, Bd. V. Parvan 4, 045B, 300223 Timisoara, Romania; [EMAIL_ADDRESS]  
[PERSON]  
2 The eAustria Research Institute, West University of Timisoara, Bd. V. Parvan 4, 045B, 300223 Timisoara, Romania; [EMAIL_ADDRESS]  
[PERSON]  
1 Department of Communications, Politechnica University of Timisoara, 2, Piata Victorieie, 300006 Timisoara, Romania; [EMAIL_ADDRESS] (C.O. ); [EMAIL_ADDRESS] (R.V.) Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/pdf/](https://www.mdpi.com/journals/pdf/)]([https://www.mdpi.com/journals/pdf/](https://www.mdpi.com/journals/pdf/))  
the dilated convolution methods have been recently proven very beneficial in many highly cited computer-vision papers: for small objects detection [6], in dense prediction tasks [7; 8], on prediction without losing resolution [9], for feature classifications in time series [8] or beneficial for context aggregation [10]. Merging those ideas we propose not just to increase the filter size but to simply dilate standard edge-detection filters in order to characterize the pixel itself through non-direct neighbor properties. We define the dilation operation for filters that consists of simply adding gaps in the well-known classical filters. This method of dilation is neither in the mathematical morphological sense [5], nor the geometric extension of the kernels discussed in the literature [11]. In our previous work we evaluated the dilation effect on classical first-order edge-detection algorithms and the classical Canny algorithm followed, naturally, with an analysis on which approach is better: to expand from lower level or to dilate, see [11; 12]. The result of the evaluation revealed that dilating the filter produces better results than expanding them. These initial set of positive results encouraged us to further investigate this topic. In Figure 1 we can observe the effect on a edge-detection algorithm when applying the dilated kernel. To better show the benefits we used green edge pixels to highlight discovered by dilating and red for the edge pixels that are lost by dilatation techniques. In order to obtain the edge maps presented in Figure 1, we used the Canny algorithm [13] with the exact parameters (Gaussian sigma value, low threshold and high threshold) and changed just the filter with a dilated version. As we can observe in the images, each level of dilation can bring with it extra edge points. This aspect can be a benefit for the overall results and in this manuscript we desire to explore this aspect. In Figure 2 we present results of Canny algorithm with different thresholds. From left to right we use a higher combination of thresholds and the rows represent the filters, from classic to dilated. As a brief interpretation of the results one can say that dilation is sensitive to artifacts at lower threshold. On the other hand, in the last column, for a higher threshold, dilated filters discover new edge points. These outcome that we see in Figures 1 and 2 encouraged us to investigate the benefits of dilating on different algorithms in the future. Figure 1: Example of edge map results using Canny algorithm configured with the same parameters. Columns are: Original image; Canny using \(3\times 3\) kernel; Canny using dilated \(5\times 5\) kernel; Canny using dilated \(7\times 7\) kernel. In this paper we desire to extend our work to other edge-detection algorithms that have as a defining step the use of edge detection kernels.