Foundations of Computer Vision (2024)
visionbook.mit.edu
visionbook.mit.edu
“This sounds like hard work.” Yes. It’s no longer about being smart. By now, everyone around you is smart. In graduate school, it’s the hard workers who pull ahead.
Many reach this realization when starting university, but some can still coast okay in college since the material to learn is well defined and upper bounded. A PhD is not really upper bounded. There's no set out amount of papers to read per week like in a college course. There's no "this won't be part of the exam". Anything is fair game. The returns on being smarter never flatten out, but simply there's no ceiling. You can always do more, read more to keep up with the literature firehose, improve your experiments, your method, etc.
You also need soft skills and a network. You need to keep your finger on the pulse of the community by going to conferences and getting to know people, grabbing coffee or going out to dinner with them. You also need to be slef driven instead of waiting for instructions like it was in college. You need to be just the right amount of skeptical and critical regarding existing methods to be able to come up with new things while being also understood and accepted and seen relevant and exciting by the community.
You also need to manage your time and set your own deadlines and maintain a routine without the external sync given by university lectures and exams. All this basically has no upper limit and even the expectations are vaguely defined. You face rejections maybe for the first time despite having done a thorough work because the reviewers don't see enough novelty or it doesn't slot neatly into what is in fashion at the moment.
My point is that a PhD can push everyone to meet their mental limits. It can be frustrating and it's a notoriously hard period of time for many PhD students. Of course if your only goal is to graduate to get the doctorate, there are possible strategies to "coast", but those who go for the academic path often expect to achieve more than the bare minimum, especially if they managed to coast with good results in college.
Computer Vision, Fifth Edition
E.R. Davies
Academic Press
ISBN-13 978-0128092842Forsyth & Ponce is also good but somewhat old by now. And for 3d, the classic is still Hartley & Zisserman's Multiple View Geometry.
I think they probably mean that LLM's just gave them a lot more to write about, but I think it would be a good idea to clarify.
Is there a Computer Vision course based on this book? Any videos, etc? Thanks!
But many other things exist outside the "glue some GPT4o vision api stuff together for a mobile app to pitch to VCs" space. Like inspecting and servicing airplanes (Airbus has vision engineers who make tools for internal use, you don't have datasets of a billion images for that). There are also things like 3D motion capture of animals, such as mice or even insects like flies, which requires very precise calibration and proper optical setups. Or estimating the meat yield of pigs and cows on farms from multi-view images combined with weight measurements. There are medical things, like cell counting, 3D reconstruction of facial geometry for plastic surgery, dentistry applications, and a million other things other than chatting with ChatGPT about images or classifying cats vs dogs or drawing bounding boxes of people in a smartphone video.
It’s great to see someone emphasize the importance of mastering the fundamentals—like calibration, optics, and lighting—rather than just chasing trendy topics like LLM or deep learning. Your examples are a great reminder of the depth and diversity in machine vision.
Your disdain for LLMs is equally puzzling. Are you seriously suggesting I shouldn’t use tools to improve my grammar and delivery simply because they don’t align with your engineering view? Ironically, LLM-based tools likely support your own work—whether through coding assistance, debugging, or other tasks—even if you choose not to acknowledge it.
By the way, I used an LLM to craft this reply too—who doesn’t?
If 'most people' are upset about others using LLMs to improve their written communication, maybe they should reflect on why they hold such outdated views—or consider that the person replying might not be a native English speaker. Are platforms like Hacker News meant only for native English speakers?
Warning: The statement above was written by an LLM, so don’t be surprised—I’m letting you know in advance.
So it's not disdain, I'm simply trying to broaden the horizon for those who only know about computer vision from OpenAI announcement and tech news and FOMO social media influencers.
Semiconductor Wafer Inspection: Detecting tiny defects like scratches or edge chips requires high-resolution cameras, precision optics, and specific lighting (e.g., darkfield) to highlight defects on reflective surfaces. Poor choices here can easily miss critical flaws.
Food Packaging Quality Control: Ensuring labels, seals, and packaging are error-free relies on the right camera and lighting. For instance, polarized lighting reduces glare on shiny surfaces, helping detect issues that might otherwise go unnoticed.