5
votes
Why do we need multiple layers in each octave and multiple octaves in SIFT?
In music theory, an octave is an interval in frequency, from a frequency $f$ to frequency $2f$. For example "an octave higher" means "twice the frequency". Expressed as wavelength ...
- 13.7k
4
votes
Why resizing an image smoothed by Gaussian by factor of 2 also increase sigma by factor of 2
A first discrete is pixel wise: imagine an image with only one active pixel. If you upsample it by two in both directions, you get a $2\times 2$ pixel block. Below, I did not extend the size of the ...
- 32.3k
3
votes
Why do we need to construct Gaussian pyramid using SIFT detector
Actually, the purpose of all this is to approximate a Laplacian of Gaussian!
This computation is part of the corner detection of SIFT. You can find corners by examining extrema of the Laplacian of ...
- 3,937
2
votes
Why is it necessary to implement octaves in sift
Yes, only scale space is sufficient, but at some point when you are scaling it down, instead of creating new Gaussian filters, it's more efficient to just resize the image and use the same/old filters ...
- 121
2
votes
Intuitive understanding of scale-space extrema detection
One of the most important characteristics of the key points is its repeatability under different geometric transformations and also lighting. Repeatability ensures that if, for example, you have two ...
2
votes
Intuitive understanding of scale-space extrema detection
LoG and DoG (an approximation of LoG) masks can serve as blob detectors. A blob can exist in an image at a number of locations $(x,y)$-coordinates and scales (some parameter; $t$). In some situation ...
- 21
2
votes
Why is scale space (DoG) needed to detect scale invariant features?
As you know, you find interest point (SIFT points) by finding the local maxima in scale-space, it mean the response of the detector must be maximum regarding the coordinates and also the scale. So ...
- 1,417
2
votes
Accepted
Why is scaling of images / pixels into `[0, 1]` range performed before SIFT (Scale Invariant Feature Transform) algorithm?
Scaling images into the [0, 1] range makes many operations more natural when using images.
It also normalizes hyper parameters such as threshold independently of ...
- 20.5k
1
vote
Accepted
The SIFT Descriptor and Image Resolution
One could show that holding the $ \sigma $ parameter constant while downsampling the image is equivalent of increasing the sigma while holding the image size constant.
Let's do a simple test:
...
- 20.5k
1
vote
What's the difference between SIFT and general stereo matching algorithm (eg, sgbm)?
After weeks of reading, researching and experiments, now I have more knowledge to answer my own question.
Both of SIFT and SGBM can be used to find matching points but they are very different in the ...
- 213
1
vote
Registration of point cloud based on feature matching method
Hope this helps because this is what I am going to do, because I have the exact same problem. If you look at the results, there is a way to do a 3 point matching between the images.
Define a matching ...
1
vote
Invariances of FFT-based Image-Registration vs. SIFT-Features
I don't understand whether these processes are also invariant to object-alterations!
They are not.
How would the extracted fft-features look, if I alter the object (scratches, marks, dents etc.)? ...
- 10.7k
1
vote
Accepted
Why do we need multiple layers in each octave and multiple octaves in SIFT?
As a starter, the 2014 IPOL paper Anatomy of the SIFT Method by Ives Rey Otero and Mauricio Delbracio provides a nice description and decryption of the SIFT method, with step-by-step pseudo-code, ...
- 32.3k
1
vote
Accepted
SIFT About Difference-of-Gaussian function extrema?
You have to take the derivative with respect to the vector $x$ and set it equal to zero. For a constant matrix $A$, the derivative of $A^Tx$ is $A$, and the derivative of $\frac12 x^TA^Tx=Ax$. So ...
- 92.5k
1
vote
How to recognize an object from a small training set of images?
Few reasons i could think of are:
Size of your training set is very small. Larger training sets have always been the key for accuracy.
Each algorithm will have some drawback like SURF is not good at ...
- 700
1
vote
What is the story behind the story about SIFT descriptor?
The descriptor obtained from a $64\times 64$ neighborhood of interest point at the obtained scale.
It will divide this $64\times 64$ region to $16\times 16$ patches which lead to 16 patches.
For ...
- 1,417
1
vote
Scale and Rotation invariant feature descriptors
If you remap a local patch around a feature point to log–polar coordinates (with the origin in the point of interest), scale changes correspond to a translation along the log–radial axis, while ...
- 111
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