Could you elaborate more on the edge detector? I thought it was a bit of a juxtaposition to go from:

> We decided to develop a customized computer vision algorithm that relies on a series of well-studied fundamental components, rather than the “black box” of machine learning algorithms such as DNNs.

To:

> To overcome these shortcomings, we used a modern machine learning-based algorithm. The algorithm is trained on images where humans annotate the most significant edges and object boundaries. Given this labeled dataset, a machine learning model is trained to predict the probability of each pixel in an image belonging to an object boundary.

This seems like a crucial step in the algorithm and sounds exactly like a black box DNN...