What are the best books for obtaining a strong understanding of computer vision? From what I understand based on my undergraduate class, almost all current state-of-the-art computer vision is just relying on deep learning, particularly convolutional neural networks.
- I've read through the Courville-Goodfellow-Bengio book, so I feel like I have a broad understanding of deep learning as a whole, but I don't feel like I have mastery over any of the specific topics that their book discusses since only the chapter about convolutional neural networks and a section about applications of convolutional neural networks really ever talk about computer vision.
- I've also read through Bishop's book on machine learning and Murphy's book on probabilistic machine learning, so I think I have a broad understanding of machine learning as a whole but am much less knowledgable about specific subdomains.
- I also see the Szeliski book on computer vision recommended to me, and I haven't read it yet. However, based on the contents, it doesn't seem like that book covers neural network usage in computer vision, and considering the recent rapid growth of the field using convolutional neural networks, I'm not sure if the material in this book would then be considered outdated. Is it still necessary to read this book (or a book of equivalent material) to be able to do state-of-the-art research in computer vision. If not necessary, would it still at least be valuable for me?
What are any other recommendations? While I would prefer more comprehensive texts, paper and survey paper recommendations are also good here too. I have a very good grasp of both pure mathematics and statistics, so references leaning on the more theoretical ends are fine too.