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Fourier Optics used for Optical Pattern Recognition

Huygens Optics5 HN recommenders · recommended in 5 months, 2021-2025
YouTube
103K views · 6 years ago · explainer · evergreen

Huygens Optics explains the Fourier transform and shows how a lens performs one optically. A DLP projector is used as a Fourier filter to do simple pattern recognition on lithographic character patterns.

Recommended as one of the best explanations of Fourier transforms and a clear demo of analog optical computing.

Everyday physics & science demosInformation theory & signal processingComputer vision, speech & NLP#fouriertransform#opticalcomputing#patternrecognition#dlpprojector#lenses

9 comments from Hacker News

@TeMPOraL2025 · in “Pre-Trained Large Language Models Use Fourier Features for Addition (2024)”

IDK, the more I learn the more it seems to me that Fourier transform is reality's cheat code. It keeps showing up everywhere. Like, the other day I learned[0] that if you shine a light through a small opening, the diffraction pattern you get on the other side is basically the Fourier transform of the aperture outline. (Yes, this also implies that if you take a Fourier transform of an image and make a diffraction grating off the resulting pattern, projecting light through it should paint you the original image.) -- [0] - ▶ this video

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@infogulch2025 · in “Detecting edges of images at the speed of light”

Huygens Optics has a great video "Fourier Optics used for Optical Pattern Recognition". He starts with explaining the Fourier Transform wrt audio, then demonstrates a physical filter that screens the resulting convolution pattern to identify matches. Very cool: ▶ this video

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@CamperBob22024 · in “AI Needs Enormous Computing Power. Could Light-Based Chips Help?”

You could think of a lens as a "Fourier transformer," not in the LLM sense directly but in the sense of something that executes a Fourier transform. See the excellent videos by Huygens Optics including this one: ▶ this video entitled "Fourier optics used for optical pattern recognition." Having established that, well, it's not hard to imagine that something that's good at running Fourier transforms is probably good for other situations where you need to run a lot of dot products in parallel. That, in turn, should sound like an awfully familiar problem...

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@Balgair2024 · in “Fluorite lenses: Corrective capabilities beyond ordinary optical glass”

Upping a cousin comment. ▶ this video Adding that, again, optics is a difficult and frustrating field. Don't worry that you're struggling to connect it. It took me a few years in an optics lab working hands on with light every day to really come around.

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@aaaaaaaaaaab2022 · in “AI-designed camera only records objects of interest while being blind to others”

Optical pattern recognition is an established field. Here's a simple version via Fresnel zone plates: ▶ this video

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@jstrieb2022 · in “AI-designed camera only records objects of interest while being blind to others”

Reminds me of this really cool video about using Fourier Optics for optical pattern recognition.[1] The video happens to have one of the best explanations of Fourier transforms I've yet encountered. 1: ▶ this video

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@orlp2022 · in “Photonic Chip Performs Image Recognition at the Speed of Light”

For a somewhat related topic (analog image recognition), for those interested, I can highly recommend this video: ▶ this video

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@ulnarkressty2022 · in “Physicists are building neural networks out of vibrations, voltages and lasers”

An even better one - holding an image at the focal point of a lens produces its Fourier transform at the focal point on the other side of the lens[0]. It is used for "analog" pattern matching[1]. There is an interesting video explaining this on the Huygen Optics Youtube channel[2]. [0] - link [1] - link [2] - ▶ this video

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@jstrieb2021 · in “Moiré No More”

I was introduced to this idea in a really cool video about using Fourier Optics for optical pattern recognition.[0] The video happens to have one of the best explanations of Fourier transforms I've yet encountered. [0] ▶ this video

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