Comment by vincenthwt
5 months ago
Can anyone recommend a good book on Machine Vision? I believe the foundation of effective machine vision, and even computer vision, lies in selecting the right camera, optics, and lighting. High-quality images are essential because poor input leads to poor output.
Hi, could you mention a use-case or two where these things made a real difference?
The term "machine vision" is mainly used in highly controlled, narrow industrial applications, think factory assembly lines, steel inspection, monitoring for cracks in materials, shape or size classification of items, etc. The task is usually very well defined, and the same thing needs to be repeated under essentially the same conditions over and over again with high reliability.
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.
Thank you for your thoughtful comment! I completely agree.
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.
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Your disdain for LLMs is unfounded.
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Here are two examples where the right camera, optics, and lighting make a huge difference:
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.
Any serious production inspection.