This is really solid intro to computer vision, bravo!
By the Power of Grayscale
41–50 of 56 posts
Re: By the Power of Grayscale
#42Really 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
Image pyramids are a brilliant method. The technique is hiding in many of the FCNN image segmentation models ive read.
A truly clever image processing method.
Re: By the Power of Grayscale
#43I was working on a image editor on the browser, https://victorribeiro.com/customFilter Right now the neat future it have is the ability of running custom filters of varied window size of images, and use custom formulas to blend several images I don't have a tutorial at hand on how to use it, but I have a YouTube video where I show some of its features https://youtube.com/playlist?list=PL3pnEx5_eGm9rVr1_u1Hm_LK6...
I vaguely remember XnView having this matrix based custom filters.
Re: By the Power of Grayscale
#44Appreciate the old school non-AI approach.
Classical machine vision and pattern recognition is absolutely AI. Or at least it was AI before it became too mature to be called that. As they say, any AI problem that gets solved stops being AI and becomes just normal algorithmics.
Re: By the Power of Grayscale
#45About 15, 20 years ago I was still in uni and we had a computer vision lab, the main guy there had been working on that subject for years and dealt with businesses where his stuff was used for quality control.
Without fail, step one of computer vision was to bring the image down to grayscale and / or filter for specific colours so you ended up with a 1 bit representation.
My "algorithm" for a robot that was to follow a line drawn on the floor boiled down to "filter out the colour green, then look at the bottom rows of the image and find the black pixels. If they're to the left, adjust to the left, if to the right adjust to the right". Roughly. I'm sure it could be done a lot more cleverly but I was pretty proud of it AND the whole tool suite was custom made, from editing environment to programming language. Expensive cameras and robot, too.
Re: By the Power of Grayscale
#46The blob-finding algorithm makes me think of the "advent of code" problems - I wouldn't have thought to do a two-pass approach, but now that I see it set out in front of me it's obviously a great idea. Seems like this technique could quite easily be generalised to work with a range of problems.
Re: By the Power of Grayscale
#47This title is excellent.
Re: By the Power of Grayscale
#48Appreciate the old school non-AI approach.
Classical machine vision and pattern recognition is absolutely AI. Or at least it was AI before it became too mature to be called that. As they say, any AI problem that gets solved stops being AI and becomes just normal algorithmics.
Classical computer vision is no more AI than quicksort or BFS is. What they say is ML is AI that works. But classic computer vision (CV) is hand rolled algorithms like Eigenfaces to detect faces or Mixture of Gaussians for background subtraction. There's no magic black box model in classic CV, no training on data, no generated pile of "if"s that no one knows how it works. Just linear algebra written and implemented by hand.
Not AI, not even ML.
Re: By the Power of Grayscale
#49Earlier quoted context omitted.
Classical machine vision and pattern recognition is absolutely AI. Or at least it was AI before it became too mature to be called that. As they say, any AI problem that gets solved stops being AI and becomes just normal algorithmics.
Classical computer vision is no more AI than quicksort or BFS is. What they say is ML is AI that works. But classic computer vision (CV) is hand rolled algorithms like Eigenfaces to detect faces or Mixture of Gaussians for background subtraction. There's no magic black box model in classic CV, no training on data, no generated pile of "if"s that no one knows how it works. Just linear algebra written and implemented b…
You might say that the loss function is the human in the loop deciding whether or not the algorithm addresses the problem.
Re: By the Power of Grayscale
#50Earlier quoted context omitted.
You're absolutely right, deep learning OCR often delivers better results for complex tasks like handwriting or noisy text. It uses advanced models like CNNs or CRNNs to learn patterns from large datasets, making it highly versatile in challenging scenarios. However, if I can’t understand the system, how can I debug it if there are any issues? Part of an engineer's job is to understand the system they’re working with,…
No matter how much you tinker and debug, classical methods can’t match the accuracy of deep learning. They are brittle and require extensive hand-tuning. What good is being able to understand a system if this understanding doesn’t improve performance anyway?
This is a classic trade-off and the decision should be made based on the business and technical context that the solution exists within.