Appreciate the old school non-AI approach.
But have a look at the "Thresholding" section. It appears to me that AI would be much better at this operation.
By the Power of Grayscale
11–20 of 56 posts
Re: By the Power of Grayscale
#12Really enjoyed this article, thanks for sharing!
I had recently learned about using image pyramids[1] in conjunction with template matching algorithms like SAD to do simple and efficient object recognition, it was quite fun.
1: https://en.wikipedia.org/wiki/Pyramid_%28image_processing%29
Re: By the Power of Grayscale
#13Didn't recognize George Smiley in those photos. Which makes sense, given he's an espiocrat.
Re: By the Power of Grayscale
#14I’m not a “C” person but I’ve really enjoyed reading this, it’s quite approachable and well written. Thank you for writing it.
Re: By the Power of Grayscale
#15Referencing "By the power of Grayskull!"
As an aside, "For the honor of grayscale" would work no worse here.
Re: By the Power of Grayscale
#16Re: By the Power of Grayscale
#17Appreciate the old school non-AI approach.
But have a look at the "Thresholding" section. It appears to me that AI would be much better at this operation.
It really depends on the application. If the illumination is consistent, such as in many machine vision tasks, traditional thresholding is often the better choice. It’s straightforward, debuggable, and produces consistent, predictable results. On the other hand, in more complex and unpredictable scenes with variable lighting, textures, or object sizes, AI-based thresholding can perform better.
That said, I still prefer traditional thresholding in controlled environments because the algorithm is understandable and transparent.
Debugging issues in AI systems can be challenging due to their "black box" nature. If the AI fails, you might need to analyze the model, adjust training data, or retrain, a process that is neither simple nor guaranteed to succeed. Traditional methods, however, allow for more direct tuning and certainty in their behavior. For consistent, explainable results in controlled settings, they are often the better option.
Re: By the Power of Grayscale
#18Earlier quoted context omitted.
But have a look at the "Thresholding" section. It appears to me that AI would be much better at this operation.
sure, if you don't mind it hallucinating different numbers into your image
Right, but the non-deep learning OCR methods also do that. And they have a much much lower overall accuracy.
There’s a reason deep learning took over computer vision.
Re: By the Power of Grayscale
#19It may come as a surprise to some that a lot of industrial computer vision is done in grayscale. In a lot of industrial CV tasks, the only things that matter are cost, speed, and dynamic range. Every approach we have to making color images compromises on one of those three characteristics.
I think this kind of thing might have real, practical use cases in industry if it's fast enough.
Re: By the Power of Grayscale
#20Appreciate the old school non-AI approach.
But have a look at the "Thresholding" section. It appears to me that AI would be much better at this operation.
It indeed would be much better.
There’s a reason the old CV methods aren’t used much anymore.
If you want to anything even moderately complex, deep learning is the only game in town.