# Tag Info

### 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 ...

### 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 ...

### 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 ...

### 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 ...

### 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 ...

### 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 ...
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 ...
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: ...
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 ...
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.)? ...
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 ...
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 ...
1 vote
Accepted

### Hessian Matrix. Second partial derivative test

The function is fully approximated if one uses all the derivatives (see Taylor expansion). With using the Hessian only, we can only make a second degree approximation (because it is second derivative ...
1 vote
Accepted

### What Is "Description Vector" in Image Processing?

I think you have a matrix. Each Row / Column is a descriptor vector of a point in the image. Just like having features, let's say M features, and each point has M values corresponding to M features. ...
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 vote

### 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 ...
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 ...

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