Drexel dragonThe Math ForumDonate to the Math Forum



Search All of the Math Forum:

Views expressed in these public forums are not endorsed by Drexel University or The Math Forum.


Math Forum » Discussions » Math Topics » discretemath

Topic: Help on SIFT
Replies: 0  

Advanced Search

Back to Topic List Back to Topic List  
Witzbold

Posts: 1
Registered: 11/17/09
Help on SIFT
Posted: Nov 17, 2009 1:04 PM
  Click to see the message monospaced in plain text Plain Text   Click to reply to this topic Reply

Hello,
being new to the Math Forum, please let me know if this is the wrong place for the question.
I am trying to re-invent the wheel and implement the SIFT algorithm (no Matlab, all I have is pure C#).
Situation: Currently, I am producing the Gauss and the DoG pyramids, and then I detect extrema within neighbored DoGs and localize them. So far so good, but I am very uncertain about the results.
So here I have a bunch of questions (again, forgive me if I am wrong here):
* From all the published papers I have found on the internet, it is not 100% clear (to me) how to build the Gauss pyramid. Is the original image already part of it, or does it start with the first blured image? I implemented as follows:
- Say I determine 2 octaves with 2 intervals.
- Blur original with sigma = 0.5 and copy it as Gauss image #1
- Blur #1 with sigma = 1.4 => Gauss image #2
- Blur #1 with sigma = 1.98 => Gauss image #3
- Subract #3 - #2 => DoG image #1
- Blur #1 with sigma = 2.8 => Gauss image #4
- Subtract #4 - #3 => DoG image #2
- Continue to
- Blur #1 with sigma = 5.6 => Gauss image #6
- Subract #6 -#5 => DoG image #4
- Re-sample Gauss image #4 (sigma is 2*1.4) to #1 Gauss image in 2nd Octave
What do you think? Is the algorithm correctly interpreted this way?

* Subtraction of images means pixel subtraction. However,
calculating the pixels already has a trade off regarding the accuracy, right? So would it help the accuracy to calculate the whole pyramides as floating arrays instead of images?

* Are there any ways at all to determine the accuracy of the implemented algorithm steps?

* I experimented, manipulating the same image (human face) slightly (softening, noise add, rotate 45 degrees, etc.), and I manually count the extrema which are "obviously" identical. I found 15-30% identity. Is this a ratio to be expected up to this stage? To me, it seems quite low.

* On another example ( house front ), my keypoints are more or less randomly distributed, while in other papers I have seen keypoints mainly close to the window edges.

Thank you for any comments in advance,
Fritz



Point your RSS reader here for a feed of the latest messages in this topic.

[Privacy Policy] [Terms of Use]

© Drexel University 1994-2014. All Rights Reserved.
The Math Forum is a research and educational enterprise of the Drexel University School of Education.