Hope you enjoy this post, and keep tuned as we have other posts to be published in coming weeks about other part of our scanning feature.
> 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...
Great work. Laurent
As the sibling says, this sounds like a good random forest problem, so you just pass in a load of patches that have been labelled with ground truth and let the classifier give you a probability for each pixel?