4 ms·

Very interesting! Any links for further reading?

by folli 4y ago

pifm_guy 4y ago
Nope - years ago I accidentally discovered this while trying to align star images for stacking. Some of my images were taken in daylight, and I was surprised to find my rudimentary image aligner still worked just fine. Never wrote it up into a blog post.
- 4gotunameagain 4y ago
  Do you mind expanding a bit more ? Because I don't understand. Even if you have oversampling, as you say, it would be after you know the locations of the stars, and also, how can you brute force every possible right ascension/declination/rotation ? Without a calibrated camera how do you account for the distortions ? Thanks
  - pifm_guy 4y ago
    In my case, I had nighttime images from the same camera, so all the calibration was already done... I was just looking for a rotated version of the nighttime image in the daytime image. But even in the general case, smallish image patches don't have enough distortion to matter, while still having plenty of oversampling, and only have 3 unknowns. You can probably do some kind of fft trickery to reduce it to 1 unknown. And you can probably use some kind of hill-climbing to further reduce the search effort.
    - 4gotunameagain 4y ago
      What does an optimization algorithm have to do with a brute force search? Even if your image patch is small, without previous images to compare, you need to brute force the entire sky? And how can "fft trickery" reduce the attitude state to 1 unknown ? 1 unknown with what units ? Sorry but I suspect you are making all these up.
      - protastus 4y ago
        The claims from pifm_guy are reasonable. This is a scenario with extremely high coding gain[1]. The DC value of the sky can be removed by high pass filtering. Finding the rigid transform (rotation+translation) between two images captured by the same camera, assuming no distortion[2], assuming the image pair has substantial intersection, doesn't require calibration. Dropping the "same camera" and "substantial intersection" assumptions requires searching the entire sky and also finding an unknown scaling factor. It all comes down to the images having low enough noise and high enough resolution, relative to the search space. The math is sound. You're not entitled to a tutorial in signal processing, though. [1] https://en.wikipedia.org/wiki/Coding_gain https://en.wikipedia.org/wiki/Coding_gain [2] Astronomy lenses have very small FOV and very small distortion.
        4gotunameagain 4y ago
        If the "DC value of the sky" could be removed with a simple high pass filter, then there would be no need of doing anything else. The positions of the stars in the sky would be known, and techniques like the one in the article would apply. I have been working fine so far in startrackers for spacecraft without your tutorial in signal processing, but thanks for the offer
        protastus 4y ago
        > If the "DC value of the sky" could be removed with a simple high pass filter, then there would be no need of doing anything else. The positions of the stars in the sky would be known, and techniques like the one in the article would apply. This doesn't make any sense. Even if one could estimate ground truth DC, removing it would leave noise at every other frequency bin. But this is beside the point. Your questioning isn't coming from a place of curiosity. I direct you to the HN guidelines: "Be kind. Don't be snarky. Have curious conversation; don't cross-examine."
        4gotunameagain 4y ago
        you were the one suggesting "removing the DC value of the sky with high pass filtering". anyway I'm done. have a nice day.
  - dekhn 4y ago
    I don't understand what they are saying but it's definitely true if you are careful, you can take images of the locations of the top magnitude stars, point your camera at that, take a long exposure (~15 seconds, so you need a way to keep the moving star at exactly the same pixel over the whole exposure), do some contrast scaling, and you will see point pixels lit up in the right locations. If you don't already know a star location, I'm sure you could construct a very sensitive camera and some noise reduction and do this with short exposures without any rotation.
323 4y ago
GPS works like that too, the GPS signal is so weak it's below the thermal noise floor when it arrives at the receiver, but you know each satellite's special signal pattern, so you just search all satellites at all positions in the noise and see if you get some correlation (a fix).

Very interesting! Any links for further reading?

by folli 4y ago

Very interesting! Any links for further reading?

Nope - years ago I accidentally discovered this while trying to align star images for stacking. Some of my images were taken in daylight, and I was surprised to find my rudimentary image aligner still worked just fine. Never wrote it up into a blog post.

4gotunameagain 4y ago
Do you mind expanding a bit more ? Because I don't understand. Even if you have oversampling, as you say, it would be after you know the locations of the stars, and also, how can you brute force every possible right ascension/declination/rotation ? Without a calibrated camera how do you account for the distortions ? Thanks
- pifm_guy 4y ago
  In my case, I had nighttime images from the same camera, so all the calibration was already done... I was just looking for a rotated version of the nighttime image in the daytime image. But even in the general case, smallish image patches don't have enough distortion to matter, while still having plenty of oversampling, and only have 3 unknowns. You can probably do some kind of fft trickery to reduce it to 1 unknown. And you can probably use some kind of hill-climbing to further reduce the search effort.
  - 4gotunameagain 4y ago
    What does an optimization algorithm have to do with a brute force search? Even if your image patch is small, without previous images to compare, you need to brute force the entire sky? And how can "fft trickery" reduce the attitude state to 1 unknown ? 1 unknown with what units ? Sorry but I suspect you are making all these up.
    - protastus 4y ago
      The claims from pifm_guy are reasonable. This is a scenario with extremely high coding gain[1]. The DC value of the sky can be removed by high pass filtering. Finding the rigid transform (rotation+translation) between two images captured by the same camera, assuming no distortion[2], assuming the image pair has substantial intersection, doesn't require calibration. Dropping the "same camera" and "substantial intersection" assumptions requires searching the entire sky and also finding an unknown scaling factor. It all comes down to the images having low enough noise and high enough resolution, relative to the search space. The math is sound. You're not entitled to a tutorial in signal processing, though. [1] https://en.wikipedia.org/wiki/Coding_gain https://en.wikipedia.org/wiki/Coding_gain [2] Astronomy lenses have very small FOV and very small distortion.
      - 4gotunameagain 4y ago
        If the "DC value of the sky" could be removed with a simple high pass filter, then there would be no need of doing anything else. The positions of the stars in the sky would be known, and techniques like the one in the article would apply. I have been working fine so far in startrackers for spacecraft without your tutorial in signal processing, but thanks for the offer
        protastus 4y ago
        > If the "DC value of the sky" could be removed with a simple high pass filter, then there would be no need of doing anything else. The positions of the stars in the sky would be known, and techniques like the one in the article would apply. This doesn't make any sense. Even if one could estimate ground truth DC, removing it would leave noise at every other frequency bin. But this is beside the point. Your questioning isn't coming from a place of curiosity. I direct you to the HN guidelines: "Be kind. Don't be snarky. Have curious conversation; don't cross-examine."
        4gotunameagain 4y ago
        you were the one suggesting "removing the DC value of the sky with high pass filtering". anyway I'm done. have a nice day.
- dekhn 4y ago
  I don't understand what they are saying but it's definitely true if you are careful, you can take images of the locations of the top magnitude stars, point your camera at that, take a long exposure (~15 seconds, so you need a way to keep the moving star at exactly the same pixel over the whole exposure), do some contrast scaling, and you will see point pixels lit up in the right locations. If you don't already know a star location, I'm sure you could construct a very sensitive camera and some noise reduction and do this with short exposures without any rotation.

323 4y ago

GPS works like that too, the GPS signal is so weak it's below the thermal noise floor when it arrives at the receiver, but you know each satellite's special signal pattern, so you just search all satellites at all positions in the noise and see if you get some correlation (a fix).