Hi, could you mention a use-case or two where these things made a real difference?
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upset about others using LLMs to improve their written communication, maybe they should reflect on why they hold such outdated views—or consider that the person replying might not be a native English speaker.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.