I read the very interesting and informative article on image noise located here:
http://www.cambridgeincolour.com/tutorials/image-noise-2.htm
One of the key points is noise frequency which can be high or low. I was curious as to how this would be calculated?
I came across another article in which the developer achieved low frequency iage noise by sampling the surrounding pixels and then performing an average. But this requires a separate calculation pass after all the noise has been calculated.
Is this how it's traditionally done or is there a different way to calculate image noise frequency?
Thanks
Look up Perlin noise.
"Salt and pepper noise" is white or black pixels scattered at random over the source. It's a fairly common sort of noise, but not useful for much visually. It has the highest frequency that the image sampling supports.
Perlin noise consists of noise of several frequencies, or "octaves" superimposed on each other. It is useful, with various parameters it looks like wood, or clouds, or swirling lava. It can also be used a bit more subtly to give slightly rugged effects to non-smooth surfaces.
The frequency is simply the distance in pixels at which existence of noise at one pixel bears no relation to the noise at the other pixel (for non-repeating noise) or the "repeat length" for repeating noise.
Related
I'm trying to model non-white noise in XMDS, but not sure how to which type of noise vector to use, or how to encode the memory function of the noise.
I'm trying to put in a noise xi(t), where <xi(t)>=0 and <xi(t)xi(t+tau)>=a*exp[|tau|/tn], where tn is the correlation time of the noise and a is its strength. It seems to me that all the noise types available assume white noise.
Any ideas?
I would like to know how noise can be removed from data (say, radio data that is an array of rows and columns with each data point representing intensity of the radiation in the given frequency and time).The array can contain radio bursts. But many fixed frequency radio noise also exists(RFI=radio frequency intereference).How to remove such noise and bring out only the burst.
I don't mean to be rude, but this question isn't clear at all. Please sharpen it up.
The normal way to remove noise is first to define it exactly and then filter it out. Usually this is done in the frequency domain. For example, if you know the normalized power spectrum P(f) of the noise, build a filter with response
e/(e + P(f))
where e<1 is an attenuation factor.
You can implement the filter digitally using FFT or a convolution kernel.
When you don't know the spectrum of the noise or when it's white, then just use the inverse of the signal band.
I am making a finger plethysmograph(FP) using an LED and a receiver. The sensor produces an analog pulse waveform that is filtered, amplified and fed into a microcontroller input with a range of 3.3-0V. This signal is converted into its digital form.
Smapling rate is 8MHz, Processor frequency is 26MHz, Precision is 10 or 8 bit.
I am having problems coming up with a robust method for peak detection. I want to be able to detect heart pulses from the finger plethysmograph. I have managed to produce an accurate measurement of heart rate using a threshold method. However, the FP is extremely sensitive to movement and the offset of the signal can change based on movement. However, the peaks of the signal will still show up but with varying voltage offset.
Therefore, I am proposing a peak detection method that uses the slope to detect peaks. In example, if a peak is produced, the slope before and after the maximum point will be positive and negative respectively.
How feasible do you think this method is? Is there an easier way to perform peak detection using a microcontroller?
You can still introduce detection of false peaks when the device is moved. This will be present whether you are timing average peak duration or applying an FFT (fast Fourier Transform).
With an FFT you should be able to ignore peaks outside the range of frequencies you are considering (ie those < 30 bpm and > 300 bpm, say).
As Kenny suggests, 8MHz might overwhelm a 26MHz chip. Any particular reason for such a high sampling rate?
Like some of the comments, I would also recommend lowering your sample rate since you only care about pulse (i.e. heart rate) for now. So, assuming you're going to be looking at resting heart rate, you'll be in the sub-1Hz to 2Hz range (60 BPM = 1Hz), depending on subject health, age, etc.
In order to isolate the frequency range of interest, I would also recommend a simple, low-order digital filter. If you have access to Matlab, you can play around with Digital Filter Design using its Filter Design and Analysis Tool (Introduction to the FDATool). As you'll find out, Digital Filtering (wiki) is not computationally expensive since it is a matter of multiplication and addition.
To answer the detection part of your question, YES, it is certainly feasible to implement peak detection on the plethysmograph waveform within a microcontroller. Taking your example, a slope-based peak detection algorithm would operate on your waveform data, searching for changes in slope, essentially where the slope waveform crosses zero.
Here are a few other things to consider about your application:
Calculating slope can have a "spread" (i.e. do you find the slope between adjacent samples, or samples which are a few samples apart?)
What if your peak detection algorithm locates peaks that are too close together, or too far apart, in a physiological sense?
A Pulse Oximeter (wiki) often utilizes LEDs which emit Red and Infrared light. How does the frequency of the LED affect the plethysmograph? (HINT: It may not be significant, but I believe you'll find one wavelength to yield greater amplitudes in your frequency range of interest.)
Of course you'll find a variety of potential algorithms if you do a literature search but I think slope-based detection is great for its simplicity. Hope it helps.
If you can detect the period using zero crossing, even at 10x oversampling of 10 Hz, you can use a line fit of the quick-n-dirty-edge to find the exact period, and then subtract the new wave's samples in that period with the previous, and get a DC offset. The period measurement will have the precision of your sample rate. Doing operations on the time and amplitude-normalized data will be much easier.
This idea is computationally light compared to FFT, which still needs additional data processing.
Given two recorded voices in digital format, is there an algorithm to compare the two and return a coefficient of similarity?
I recommend to take a look into the HTK toolkit for speech recognition http://htk.eng.cam.ac.uk/, especially the part on feature extraction.
Features that I would assume to be good indicators:
Mel-Cepstrum coefficients (general timbre)
LPC (for the harmonics)
Given your clarification I think what you are looking for falls under speech recognition algorithms.
Even though you are only looking for the measure of similarity and not trying to turn speech into text, still the concepts are the same and I would not be surprised if a large part of the algorithms would be quite useful.
However, you will have to define this coefficient of similarity more formally and precisely to get anywhere.
EDIT:
I believe speech recognition algorithms would be useful because they do abstraction of the sound and comparison to some known forms. Conceptually this might not be that different from taking two recordings, abstracting them and comparing them.
From wikipedia article on HMM
"In speech recognition, the hidden
Markov model would output a sequence
of n-dimensional real-valued vectors
(with n being a small integer, such as
10), outputting one of these every 10
milliseconds. The vectors would
consist of cepstral coefficients,
which are obtained by taking a Fourier
transform of a short time window of
speech and decorrelating the spectrum
using a cosine transform, then taking
the first (most significant)
coefficients."
So if you run such an algorithm on both recordings you would end up with coefficients that represent the recordings and it might be far easier to measure and establish similarities between the two.
But again now you come to the question of defining the 'similarity coefficient' and introducing dogs and horses did not really help.
(Well it does a bit, but in terms of evaluating algorithms and choosing one over another, you will have to do better).
There are many different algorithms - the general name for this task is Speaker Identification - start with this Wikipedia page and work from there: http://en.wikipedia.org/wiki/Speaker_recognition
I'm not sure this will work for soundfiles, but it gives you an idea how to proceed i hope. That is a basic way how to find a pattern (image) in another image.
You first have to calculate the fft of both the soundfiles and then do a correlation. In formular it would look like (pseudocode):
fftSoundFile1 = fft(soundFile1);
fftConjSoundFile2 = conj(fft(soundFile2));
result_corr = real(ifft(soundFile1.*soundFile2));
Where fft= fast Fourier transform, ifft = inverse, conj = conjugate complex.
The fft is performed on the sample values of the soundfiles.
The peaks in the result_corr vector will then give you the positions of high correlation.
Note that both soundfiles must in this case be of the same size-otherwise you have to place the shorter one into a file of max(soundFileLength) vector.
Regards
Edit: .* means (in matlab style) a component wise mult, you must not do a vector mult!
Next Edit: Note that you have to operate with complex numbers - but there are several Complex classes out there so I think you don't have to bother about this.
I love to work on AI optimization software (Genetic Algorithms, Particle Swarm, Ant Colony, ...). Unfortunately I have run out of interesting problems to solve. What problem would you like to have solved?
This list of NP complete problems should keep you busy for a while...
How about the Hutter Prize?
From the entry on Wikipedia:
The Hutter Prize is a cash prize
funded by Marcus Hutter which rewards
data compression improvements on a
specific 100 MB English text file.
[...]
The goal of the Hutter Prize is to
encourage research in artificial
intelligence (AI). The organizers
believe that text compression and AI
are equivalent problems.
Basically the idea is that in order to make a compressor which is able to compress data most efficiently, the compressor must be, in Marcus Hutter's words, "smarter". For more information on the relation between artificial intelligence and compression, see the Motivation and FAQ sections of the Hutter Prize website.
Does the Netflix Prize count?
I would like my bank balance optimised so that there is as much money as possible left at the end of the month, instead of the other way round.
What about the Go Game ?
Here's an interesting practical problem I came up while tinkering with color quantization and image compression.
The basic idea is that I would like a program to which I give a picture and it reduces the amount of colors is it as much as possible without me noticing it. Since every person has a different sensitivity of the eye (and eyes have different sensitivity of red/green/blue intensities), it should be possible to specify this sensitivity threshold in some way.
In other words, in a truecolor picture, replace every pixel's color with another color so that:
The total count of different colors in a picture would be the smallest possible; and
Every new pixel would have it's color no further from the original color than some user-specified value D.
The D can be defined in different ways, pick your favorite. For example:
Separate red, green and blue components for specifying the maximum possible deviation for each of them (for every pixel you get a rectangular cuboid of valid replacement values);
A real number which would represent the maximum allowable distance in the RGB cube (for every pixel you get a sphere of valid replacement values);
Something inbetween or completely different.
Most efficient solution to a given set of Sudoku puzzles. (excluding brute-force methods)