Spacey
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How to Extract High Frequency and Low Frequency Component Using Bilateral Filter?
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29 votes

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. ...

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Why are Gaussian filters used as low pass filters in image processing?
23 votes

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 ...

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Reading the Wavelet transform plot
15 votes

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 ...

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Time delay estimation of oscilloscope signals using cross correlation
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13 votes

@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. ...

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Peak detection approach
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13 votes

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 ...

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What does the normalization step of the Haar wavelet transform represent?
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12 votes

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, (...

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On the time-frequency relationships of median filters
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11 votes

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 ...

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Choosing the length of a signal when calculating the FFT
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10 votes

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 ...

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Using continuous verses discrete wavelet transform in digital applications
9 votes

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 ...

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Determining the type of filter on an image
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9 votes

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 ...

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Simplest way of detecting where audio envelopes start and stop
9 votes

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 ...

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Why complex numbers are represents as a+ib and can't be as (a,b)?
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8 votes

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 ...

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When should the sum of all elements of a gaussian kernel be zero?
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8 votes

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 ...

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Why transforming the data to a high-dimensional feature space in which classes are linearly separable leads to overfitting?
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8 votes

@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-...

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Why can the FFT always be mirrored in the middle of the x axis?
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7 votes

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 ...

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Determining Frequency and Period of a wave
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7 votes

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 ...

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Hilbert transform to compute signal envelope?
7 votes

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 ...

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Why Low Frequency called Approximation and High Frequency Detail?
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6 votes

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 ...

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Showing frequency and amplitude after doing an FFT on a signal
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6 votes

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, ...

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Bag of Tricks for Denoising Signals While Maintaining Sharp Transitions
6 votes

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 ...

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What Is the Relationship Between a Kalman Filter and Polynomial Regression?
6 votes

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 ...

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Finding the dominant tone in a signal
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5 votes

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; % ...

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Can edge detection be done in the frequency domain?
5 votes

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 ...

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Books that explain DSP well to those not directly in engineering?
5 votes

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 ...

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wavelet denoising routine for environmental data series
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4 votes

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 ...

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How to determine the "variability" in a frequency range?
4 votes

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'. ...

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Frequency vector and fft
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3 votes

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 $\bf{f}$,...

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What is mean by [1 -1] Filter?
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3 votes

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 ...

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Determining Type and Bandwidth of a filter
3 votes

The negative of the second derivative of a gaussian kernel as you have described it turns out to be what is called the 'Mexican Hat'. kernel. You can see some of its uses in the wiki. As it stands, ...

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Face recognition using independent component analysis (ICA)
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3 votes

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 ...

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