Comment by oytis
Can someone working in the field comment on how relevant the content still is? A lot of ML including CV seems (from the outside at least) to be completely disrupted by the developments of the last two years.
Can someone working in the field comment on how relevant the content still is? A lot of ML including CV seems (from the outside at least) to be completely disrupted by the developments of the last two years.
It still is super relevant. Most computer vision done outside academia is still based on older stuff, or classical computer vision algorithms. You don't really get so many chances to use the latest models and techniques, as most often than not, they are not that relevant, or are only for extremely specific cases, or you just don't need something that complex.
there are still a lot of problems to be solved using "classical" computer vision, especially in systems where you don't have easy access to GPU acceleration. I am a practitioner doing Simultaneous localization and mapping on compute-restricted platforms, so definitely going to read the Structure from Motion chapter.
Very relevant. None of the recent techniques are truly revolutionary. It's all based on these same foundations. I'd say it would do good to read even older ones. There are lots of real, profitable computer vision applications built on classic methods like Hough transforms, canny edges, sift, Harris corners, etc. You should be familiar with these if you want to come across as a serious professional as opposed to a hype boy vibe coder who can just rattle off buzzwords and glue apis without fundamental understanding.