

How a Programmer Can Discover an Asteroid - typpo
http://www.ianww.com/blog/2013/08/05/how-a-programmer-can-discover-an-asteroid/

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3JPLW
Interesting. Here's the link to the Asterank Discover page:
[http://asterank.com/discover](http://asterank.com/discover)

I've gone through a handful or two of images now. Although he says "The app
occasionally serves control images to get a sense of whose responses are
trustworthy," I have yet to see anything move. It'd be nice to have a higher
number of catch trials with a game-like interface. _" Congrats, you found the
moving dot! This is asteroid xxxx"_ or: _" Oops, you missed it, try again"_
or: _" Hey, this is a new one! We'll check on it and get back to you."_

I think that'd help train and reward those who look through all these images.

 _Edit: after a few dozen, I think I found one. But I don 't know if it was a
catch trial or not._

~~~
ics
Agreed. The lack of feedback is slightly troubling when you think you see one,
as is the inability to reposition or remove a dot. Still, it's quite a bit of
fun.

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mutagen
This seems like a good time to point out the Zooniverse project
([https://www.zooniverse.org/](https://www.zooniverse.org/)) which is using
similar crowdsourcing techniques in the browser to do all kinds of things from
cataloging galaxies to entering data from ships logs to extend our view of
earth's climate.

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johansch
The 3d view, [http://www.asterank.com/3d](http://www.asterank.com/3d) is
_very_ snazzy.

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obituary_latte
Quite difficult (but very cool).

The way that the brightness (if that's the right term) varies between frames
made it hard for me to distinguish between movement and adjacent objects
appearing and disappearing. I wonder if there would be a way to normalize the
brightness based on the brightest of the frames (i.e. the frame with the most
objects visible) to make it easier to detect actual movement as opposed to
what looks like movement due to stop-motion-like hiding and revealing of
adjacent objects.

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3327
Hey great project I always wanted to set set aside time for similar projects,
mainly going over through the troves of data available from the Kepler that is
publicly available. I might start a a project and put it on github but as you
all know such things are incredibly time consuming, yet are possible with a
small and dedicated team.

If anyone would like to collaborate on such a project in a dedicated and
professional manner I would love to chat.

I have 2 friends onboard (google and MS search engineers) who will put in time
too given proper dedication.

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sharemywin
I wonder if you feed a few of screenshots of the video into a Reduced Boltzman
Machine if you could get it to come up with what it thinks an astroid looks
like. seems like it would be perfect for this kind of thing.

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barcode2
mmm a "zoo" project yeah, crowdsourcing science like this is actually pretty
popular. They use it for determining galaxy types and finding planets...
unfortunately it's often highly biased once you get to the realm of Machine
Learning failure, you end up in a regime of false signals that can easily
fudge amateurs

like galaxies, crowdsourcing science found that that most (like a significant
portion of most) galaxies spin clockwise or something, but it should be purely
random. This was a big blow to the galaxy-zoo project from like 8 years ago.
crowdsourcing science is hugely powerful, but I don't think it should be
tackled or implemented by amateurs really... crowdsourcing should really be in
the form of spare CPU cycles like BOINC projects...

Although. Asteroid detection does basically operate on the algorithm this guy
proposes. emphasis on _basically_ there are literally thousands of types of
variable objects that can flicker out and thousands which can flicker in. and
a pure random background which gives strong positive signal on an
instantaneous timescale. what he's doing is basically 1960s tech with a
million grad students.

I also want to blow a load on this guy's burger for this fucking statement: "A
year ago, I set aside my doubts and started to innovate in the space industry
as a complete outsider." Give this fuck 3 hours at CfA and see how confident
he is of innovating anything. The real people (CSCI People) changing
astrophysics are the deep machine learning guys, the data structure guys and
the mapping algorithm guys. not the bloggers.

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danso
This is a worthy idea and a lovely example of programming (particularly web
development) tackling interesting problems outside of your domain...I think
there's huge potential for programmers who, arrogantly or not, attempt to
tackle industries and fields as outsiders.

However, I don't understand why the OP believes asteroids too small to be seen
through computer-vision algorithms would be noticed by human eyes? I'd think
computer processing would be much better (and configurable) at detecting the
smallest cases and, at the same time, be better (with some additional
tweaking) at reducing false positives. I'd be interested in seeing whatever
data/edge cases the OP found that led him to go this crowd-sourcing route.

(OTOH, bringing people in to help find asteroids is a great way to bring
attention to the project...but hopefully he's doing a lot of pre-
filtering/processing of the images to serve up just the most likely candidate
sequences for humans to go over...and if so, what's the threshhold he's set
for the pre-processing)

~~~
typpo
Computer algos do a good job at asteroid id'ing - they discover a few every
day - but most experts I've spoken with (including people running large sky
surveys like PanSTARRS and CSS) agree that crowdsourcing is an approach worth
trying. I've updated my post with a few of the reasons why, which generally
relate to the unreliable quality of sky survey images.

From personal experience, the algorithms and data used by sky surveys are not
readily available to the public. This inhibits innovation. Without a PhD,
there is little opportunity for me to build on state of the art and improve
them directly. I agree that with the images I've collected, there may be some
interesting algorithm/ML opportunities. Part of the reason why I started with
crowdsourcing is to get a decent training set.

I appreciate the feedback!

~~~
danso
Thanks for the reply and update! Also, I just realized you have a link to the
source repo, which would answer a few of my questions. If there are certain
conditions in which false positives arise frequently from image analysis,
perhaps a refinement of the parameters (if photo_taken_during_hazy_night && by
satellite_in_position(x,y)_relative_to_viewport, then increase
fuzziness_factor) might also cut the noise. From my layman's perspective, the
problem seems to be "easy" because of the relative simpleness of the shapes
(whitish dots on a dark area) and expected movement patterns...And so the fact
that _it isn 't_ means that there's other problems (data quality) or a major
flaw in (my) assumptions...which is even _more_ interesting.

Conversely, it'd be interesting to set up a measurement-framework to test
which conditions are users most likely to report false positives.

Anyway, these are all details that may also have been overlooked by those in
the field and that even if they realized it, they don't have a strategy (or
see the need) to perform such meta-analysis. Another way that an outsider can
bring freshness to a field.

