In the scale-space theory the scale-space representation of the signal $f(x), x = (x_1, ..., x_d)$, (in case of image $d = 2$) is given as: $L(x, y; t) = g(x, y; t) * f(x, y)$ where $g(x, y; t)$ is a gaussian kernel with parameter $t$ and $*$ is a convolution. By changing the $t$ parameter we receive a more or less smoothed image. As the result coarser representation (parameter $t$) will not contain small objects or noise.
The main point is to find a way of scale-invariant feature detection, right? So that for some image at it's reduced in size copy the features like keypoints will be detected correctly, even if size is different, without finding other noise keypoints.
In the paper they are using the $\gamma$-normalized derivatives. $\delta_{\xi, \gamma-norm} = t^{\gamma / 2} \delta_x$. What is the meaning of using the $\gamma$-normalized derivative, how does it helps in scale-invariancy?
From this image we can see that at near the same positions the different keypoints found (different in size). How is that possible?
If you can explain the step-by-step algorithm of scale-invariant feature detection, this would be great. What is actually done? The derivatives can be taken by $x, y$ or $t$. Blob can be detected by taking the the derivative of $L$ by $(x, y)$ variables. How is the derivative by $t$ is helping here?
The paper I was reading is: Feature detection with automatic scale selection