[HN Gopher] Telling the time with computer vision
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       Telling the time with computer vision
        
       Author : jinay
       Score  : 6 points
       Date   : 2021-12-18 16:25 UTC (1 days ago)
        
 (HTM) web link (blog.jinay.dev)
 (TXT) w3m dump (blog.jinay.dev)
        
       | warrenm wrote:
       | >Few things are more embarrassing than having to spend 20 seconds
       | trying to figure out how much time you have left on a
       | standardized test
       | 
       | This is a sorta-clever technique ... but if you can't have your
       | phone, this is 100% useless
       | 
       | There are only three proper ways to worry about how much time you
       | have left on a test:
       | 
       | - don't worry: keep going until you're done or you run out of
       | time (the best option)
       | 
       | - wear a watch
       | 
       | - periodically look at the countdown timer on the test (if it's
       | either being done on a computer, or if there's a timer in the
       | room)
        
         | jinay wrote:
         | You're completely right. If you had your phone you could just
         | look at the phone time anyways :)
         | 
         | As I mentioned in the conclusion, there wasn't really meant to
         | be any practical use for this project.
        
           | warrenm wrote:
           | It's certainly an amusing rabbit trail, though :)
        
           | warrenm wrote:
           | I could see a _potential_ for usefulness if you wanted to
           | categorize all of the images you had of clocks with what
           | times are listed to be able to put them in [quasi]
           | chronological order
        
             | warrenm wrote:
             | And then - maybe - auto-select a clock image that matched
             | the publish time, for example, of a blog post?
        
           | stavros wrote:
           | You know what I call a project without a practical use? Art.
        
       | W0lf wrote:
       | I appreciate that the author was looking to solve this problem
       | using a _classical_ CV approach just like in the good ol' days as
       | opposed to just train another ANN that predicts the time for a
       | sufficiently large training set.
       | 
       | As for the detection of the hands itself: I think I'd try to look
       | for them in fourier space to get a more robust result (to address
       | noisy dials like in the last examples).
        
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