I was trying to understand on how to detect the two peaks from the histogram. There can be multiple but I need to pick the two highest. Basically what I need to to is that although I will have these peaks shifted left or right, I need to get hold of them. Their spread can vary and their PEAK values might change so I have to find a way to get hold of these two peaks in Matlab.
What I have done so far is to create a 5 value window. This window is populated with values from the histogram and a scan is performed. Each time I move 5-steps ahead to the next value and compare the previous window value with current. Which ever is greater is kept.
Is there a better way of doing this?
The simplest way to do this would be to first smooth the data using a gaussian kernel to remove the high frequency variations.
Then use the function localmax to find the local maximums.
Return data from hist (or histc) function to a variable (y = hist(x,bin);) and use PEAKFINDER FileExchange submission to find local maximums.
I have also used PEAKDET function from Eli Billauer. Works great. You can check my answer here with code example.
Related
Maxpooling is a technique I read about it here https://computersciencewiki.org/index.php/Max-pooling_/_Pooling. I understand, that it is used to approximate the input. Which is to reduce the time a neural network may spend working on it. What I can't pinpoint is, why should it select the max values? is that effective? if so why?. Other options could be like selecting mean, or min, or maybe the top left values(for instance).
We select max of window to take the pixel which is most activated (more activation of a pixel means more information).
There are variations like avg-pooling to take the mean of all pixels of a window, but in practice there is not a lot of difference in the results.
Max-Pooling is effective and fast. Another reason to use max-pool over avg-pool is computing the gradient (in the backprop) will be fast for max-pooling.
Background:
Assuming there are two shots for the same scene from two different perspective. Applying a registration algorithm on them will result in Homography Matrix that represents the relation between them. By warping one of them using this Homography Matrix will (theoretically) result in two identical images (if the non-shared area is ignored).
Since no perfection is exist, the two images may not be absolutely identical, we may find some differences between them and this differences can be shown obviously while subtracting them.
Example:
Furthermore, the lighting condition may results in huge difference while subtracting.
Problem:
I am looking for a metric that I can evaluate the accuracy of the registration process. This metric should be:
Normalized: 0->1 measurement which does not relate to the image type (natural scene, text, human...). For example, if two totally different registration process on totally different pair of photos have the same confidence, let us say 0.5, this means that the same good (or bad) registeration happened. This should applied even one of the pair is for very details-reach photos and the other of white background with "Hello" in black written.
Distinguishing between miss-registration accuracy and different lighting conditions: Although there is many way to eliminate this difference and make the two images look approximately the same, I am looking of measurement that does not count them rather than fixing them (performance issue).
One of the first thing that came in mind is to sum the absolute differences of the two images. However, this will result in a number that represent the error. This number has no meaning when you want to compare it to another registration process because another images with better registration but more details may give a bigger error rather than a smaller one.
Sorry for the long post. I am glad to provide any further information and collaborating in finding the solution.
P.S. Using OpenCV is acceptable and preferable.
You can always use invariant (lighting/scale/rotation) features in both images. For example SIFT features.
When you match these using typical ratio (between nearest and next nearest), you'll have a large set of matches. You can calculate the homography using your method, or using RANSAC on these matches.
In any case, for any homography candidate, you can calculate the number of feature matches (out of all), which agree with the model.
The number divided by the total matches number gives you a metric of 0-1 as to the quality of the model.
If you use RANSAC using the matches to calculate the homography, the quality metric is already built in.
This problem is given two images decide how misaligned they are.
Thats why we did the registration. The registration approach cannot answer itself how bad a job it did becasue if it knew it it would have done it.
Only in the absolute correct case do we know the result: 0
You want a deterministic answer? you add deterministic input.
a red square in a given fixed position which can be measured how rotated - translated-scaled it is. In the conditions of lab this can be achieved.
I'm experimenting using reservoir computing techniques to classify images, but I'm not sure how to convert an arbitrary image to a time series.
I found this approach but it doesn't seem to be general enough.
Thanks!
As defined in that article, a time series is just a single-value function of one variable. However, an image is, in general, a multi-value function of two variables. So, in order to convert from an image to a 'time series', you're projecting down from a higher dimensional space to a lower dimensional one (for example, the radial scanning technique described collapses the image as a whole into an outline, which reduces the dimension to one). A key point is that these projections all 'lose data'. Since they're all lossy, there isn't going to be a 'general' solution that works for all uses of all images.. choosing what data you can afford to lose based on your intended application is a key aspect of using this technique. So, I guess my answer is that there is no single general way to convert an image to a 'time series' that works well for all applications.
I would think along those lines.
An image is a static two dimensional array of pixels recorded of a period in time.
A time series is non-static just like a video is a series of images going from one frame to the next in time.
Not sure I answered the question but I hope this helps.
I used this answer and wrote my own program, but I have a specific problem.
If the image does not have the object, matchTemplate does not throw an error, and I do not know of any method to check if matchTemplate found the object or not, can anyone give me advice, or give me a function name which checks this.
matchTemplate() returns a matrix whose values indicate the probability that your object is centered in that pixel. If you know the object (and only one object) is there, all you have to do is look for the location of the maximum value.
If you don't know, you have to find the max value, and if it is above a certain threshold, your object should be there.
Now, selection of that threshold is tricky - it's up to you to find the good threshold specifically for your app. And of course you'll have some false positives (when there is no object, but the max is bigger than threshold), and some false negatives (your object does not create a big enough peak)
The way to choose the threshold is to collect a fairly large database of images with and without your object inside, and make a statistic of how big is the peak when object is inside, and how big is when it isn't, and choose the threshold that best separates the two classes
So I've recorded some data from an Android GPS, and I'm trying to find the peaks of these graphs, but I haven't been able to find anything specific, perhaps because I'm not too sure what I'm looking for. I have found some MatLab functions, but I can't find the actual algorithms that do it. I need to do this in Java, but I should be able to translate code from other languages.
As you can see, there are lots of 'mini-peaks', but I just want the main ones.
Your solution depends on what you want to do with the data. If you want to do very serious things then you should most likely use (Fast) Fourier Transforms, and extract both the phase and frequency output from it. But that's very computationally intensive and takes a long while to program. If you just want to do something simple that doesn't require a lot of computational resources, then here's a suggestion:
For that exact problem i implemented the below algorithm a few hours ago. I invented the algorithm myself so i do not know if it has a name already, but it is working great on very noisy data.
You need to determine the average peak-to-peak distance and call that PtP. Do that measurement any what you like. Judging from the graph in your case it appears to be about 35. In my code i have another algorithm i invented to do that automatically.
Then choose a random starting index on the graph. Poll every new datapoint from then on and wait until the graph has either risen or fallen from the starting index level by about 70% of PtP. If it was a fall then that's a tock. If it was a rise then that's a tick. Store that level as the last tick or tock height. Produce a 'tick' or 'tock' event at this index.
Continue forward in the data. After ticks, if the data continues to rise after that point then store that level as the new 'height-of-tick' but do not produce a new tick event. After tocks, if the data continues to fall after that point then store that level as the new 'depth-of-tock' but do not produce a new tock event.
If last event was a tock then wait for a tick, if last event was a tick then wait for a tock.
Each time you detect a tick, then that should be a peak! Good luck.
I think what you want to do is run this through some sort of low-pass filter. Depending on exactly what you want to get out of this dataset, a simple "box car" filter might be
sufficient: at each point, take the average of the N samples centered on that point,
and take the average as the filtered value. The larger N is, the more aggressively smoothed the filtered data will be.
I guess you have lots of points... Calculate mean value of them, subtract it from all point's values and get highest point value (negative or positive) from each range where points have same sign till they change it. I hope I am clear...
With particulary nasty and noisy data I usually use smoothing. Easiest example of smoothing is moving average. Then you can find peacks on that moving average. And then you simply go back to your original data and take the closest peak to one you found on moving average.
I've done some looking into peak detection and I can tell you that if your data doesn't behave, it could mess up your algorithm. Off the top of my head, you could try: Pick a threshold, i.e threshold = 250. If data is above threshold, find the max at that period. This is assuming that the data you have has a mean about 230. Not sure how fancy you want to get. Hope that helps.