Spacey
• Member for 10 years, 4 months
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Similar to one dimensional signals, low frequencies in images mean pixel values that are changing slowly over space, while high frequency content means pixel values that are rapidly changing in space. ...

Gaussian filters are used in image processing because they have a property that their support in the time domain, is equal to their support in the frequency domain. This comes about from the Gaussian ...

Imagine for one second, that you just plotted your daubechies-4 wavelet, as you can see here in red. Now imagine that you take this waveform in red, and simply do a cross-correlation with your ...

@NickS Since it is far from certain that the second signal in the plots is in fact a solely delayed version of the first, other methods besides the classical cross-correlation have to be attempted. ...

Ktuncer, there are a number of methods you can use here. One method that I would recommend is to use a Discrete Wavelet Transform, (DWT), and in particular, look at the Daubechies Wavelet. I would ...

As I understand it, the normalization is because the Haar wavelet conserves energy of the signal. In that, when you take signal from one domain to another, you aren't supposed to add energy to it, (...

The median filter is actually an example of a non-linear filtering operation. This stands in contrast to linear filtering operations (the 'classical' way that involve the convolution of a filter's ...

Mai, The length of the FFT depends on what application you are doing. A very course summary follows: Same size FFT: Analysis: This just means you want to 'analyse' the signal - look and see what ...

A very common yet unfortunate mis-conception in the field of wavelets has to do with the ill-coined terminology of "Continuous Wavelet Transforms". First thing's first: The Continuous Wavelet ...

As you said, you can simply plot the frequency response of your filter from $-\pi$ to $+\pi$. However since this is a filter only has real co-efficients, you can also just plot the frequency ...

Eric, If you are truly after something quick and dirty, the first thing you have to get is the envelope, and I would do this simply (in MATLAB) by: envelope = abs(hilbert(yourSignal)); At that ...

Yes, in signal processing, complex numbers are usually visualized on the complex plane, as you have said. The reason is that if you put them on a plane, then you are able to measure two important ...

The sum of a gaussian kernel cannot be zero, because all the elements are going to be positive. The first kernel you have shown, is most likely an edge detection kernel, (which is a type of high pass ...

@ffriend has a good post about it, but generally speaking, if you transform to a high dimensional feature space and train from there, the learning algorithm is 'forced' to take into account the higher-...

The DFT of a real signal is conjugate symmetric. For example, if your DFT result at, say, 2Hz was $1+j5$, then your DFT result at -2Hz would be $1-j5$. This is conjugate symmetry. Of course, when ...

Interesting project you have going on there! :-) From a signal analysis POV, this is actually a simple question - and yes, you are right that you would utilize the FFT for this frequency estimation ...

You can use the Hilbert transform to compute an envelope in the following way. (I will write it as MATLAB code): envelope = abs(hilbert(yourTimeDomainSignal)); I do not have time to write the math ...

Intuitively speaking, anything that is 'high frequency' is something that is 'rapidly changing in time'. Anything that is 'low frequency' is something that is 'slowly changing in time'. If you think ...

A strong amplitude response at 0 Hz simply means that you have a very strong DC offset. In other words, it just means that the mean of your signal is not 0. If this is the only problem you have, ...

Chaohuang has a good answer, but I will also add that one other method that you can use would be via the Haar Wavelet Transform, followed by wavelet co-efficient shrinkage, and an Inverse Haar ...

Not an expert on kalman filters, however I believe traditional Kalman filtering presumes a linear relationship between the observable data, and data you wish to infer, in contrast to more intricate ...

I think you are having trouble constructing your frequency axis properly. Once you do this, you can do a simple peak pick. I have re-written the code for you: Fs = 48000; x = dataSet; % ...

Usually the edge detection is done by a convolution of a 2-D filter/kernel like Roberts Cross or a Sobel formulation. Since those are convolutions, LTI rules apply, like being able to equivalently ...

I am surprised no one has mentioned Richard Lyon's book - by far one of the BEST books out there on understanding digital signal processing in a very clear, concise, and methodological way. Its ...

First, a comment - before you denoise, you are basically going to be converting your data from the (time)-domain into the wavelet domain. This is nothing but a series of projections of your data unto ...

Daniel, Upon re-reading your question, it appears that what I have learned to be known as the 'Gabor Bandwidth" might be useful to you in this case, for you trying to measure 'spectral variability'. ...

The proper way to define your frequency vector after a DFT is as follows. Let $N$ be your DFT length, and $f_s$ be your sampling rate in Hz. Furthermore, define an $N$-length frequency vector $\bf{f}$,...

I have the same book as you. :-) The [1 -1] filter is a a simple differentiator, also known as, the Haar mother wavelet. You need to understand convolution to understand what he is saying. On the ...

For your data set of images, first vectorize the images by raster scanning them, and making them vectors. Thus, say you have $M$ images, each of size 64*64 pixels. Then the total number of pixels per ...