Dilated filters for edge detection algorithms
This work addresses edge detection for image processing applications, but it appears incremental as it builds on existing dilated convolution techniques.
The paper tackled edge detection in image processing by proposing dilated filters as a replacement for classical convolution filters, and experimental results showed that dilation positively impacts edge detection algorithms from simple to complex ones.
Edges are a basic and fundamental feature in image processing, that are used directly or indirectly in huge amount of applications. Inspired by the expansion of image resolution and processing power dilated convolution techniques appeared. Dilated convolution have impressive results in machine learning, we discuss here the idea of dilating the standard filters which are used in edge detection algorithms. In this work we try to put together all our previous and current results by using instead of the classical convolution filters a dilated one. We compare the results of the edge detection algorithms using the proposed dilation filters with original filters or custom variants. Experimental results confirm our statement that dilation of filters have positive impact for edge detection algorithms form simple to rather complex algorithms.