I thought this should be a simple task, I just can't find the way to do it:
I am using 'imregister' (MATLAB) to register two medical X-ray images.
To ensure I get the best registration outcome as possible, I use some image processing techniques such as contrast enhancement, blackening of objects that are different between images and even cropping.
The outcome of this seems to be quite satisfying.
Now, I want to perform the exact same registaration on the original images, so that I can display the two ORIGINAL images automatically in alignment.
I think that an output parameter such as tform serves this purpose of performing a certain registration on any two images, but unfortunately 'imregister' does not provide such a parameter, as far as I know.
It does provide as an output the spatial referencing object R_reg which might be the answer, but I still haven't figured out how to use it to re-preform the registration.
I should mention that since I am dealing with medical X-ray images on which non of the feature-detecting algorithms seem to work well enough to perform registration, I can only use intensity-based (as opposed to feature-based) registration, and therefore am using 'imregister'.
Does anyone know how I can accomplish this?
Thanks!
Noga
So to make an answer out of my comment, there are 2 things you can do depending on the Matlab release you are using:
Option 1: R2013a and earlier
I suggest modifying the built-in imregister function by forcing tform to be an output and save that function under another name.
For example:
function [movingReg,Rreg,tform] = imregister2(varargin)
save that, add it to your path and you're good to go. If you type edit imregister you will notice that the 1st line calls imregtform to get the geometric transformation required, while the last line calls imwarp, to apply that geometric transformation. Which leads us to Option 2.
Option 2: R2013b and later
Well in that case you can directly use imregtform to get the tform object and then use imwarpto apply it. Easy isn't it?
Hope that makes things clearer!
Related
I want to integrate a Modelica variable over time, just for convenience in plotting and post-processing. The variable I want to integrate over time is the power of a compressor so that I get the total energy. The first idea would be to add these lines:
Modelica.Units.SI.Power P_comp;
Modelica.Units.SI.Energy E_comp;
equation
P_comp = der(E_comp);
Is that the recommended way, or are there (better?) alternatives? Is it expected to influence the selection of dynamic states?
Assuming that those two lines are the only ones using E_comp that should work.
Basically E_comp will be part of its own separate state-selection block and changes there shouldn't influence anything else.
However, state selection consists of a number of algorithms and heuristics so it is difficult to formally guarantee that any change does not influence it.
I could imagine some strange possibilities that would break this, but I don't think anyone has implemented them - and I don't see a use-case for them (except to mess up cases like this).
And if you instead of integrating want to differentiate a signal it is a lot messier.
I am simulating the case "Cavity driven lid" and I try to get all the stream lines with the stream tracer of paraview, but I only get the ones that intersect the reference line, and because of that there are vortices that are not visible. How can I see all the stream-lines in the domain?
Thanks a lot in adavance.
To add a little bit to Mathieu's answer, if you really want streamlines everywhere, then you can create a Stream Tracer With Custom Source (as Mathieu suggested) and set your data to both the Input and the Seed Source. That will create a streamline originating from every point in your dataset, which is pretty much what you asked for.
However, while you can do this, you will probably not be happy with the results. First of all, unless your data is trivially small, this will take a long time to compute and create a large amount of data. Even worse, the result will be so dense that you won't be able to see anything. You will get all those interesting streamlines through vortices, but they will be completely hidden by all the boring streamlines around them.
Thus, you are better off with trying to derive a data set that contains seed points that are likely to trace a stream through the vortices that you are interested in. One thing you might want to try is to compute the vorticity of your vector field (Gradient Of Unstructured Data Set when turning on advanced option Compute Vorticity), find the magnitude of that (Calculator), and then use the Threshold filter to pull out the cells with large vorticity. Then use that as your Seed Source.
Another (probably better) option if your data is 2D or you can extract an interesting surface along the flow of your data is to use the Surface LIC plugin. Details can be found at https://www.paraview.org/Wiki/ParaView/Line_Integral_Convolution.
You have to choose a representative source for your streamline.
You could use a "Sphere Source", so in the StreamTracer properties.
If that fails, you can use a StreamTracerWithCustomSource and use your own source that you will have to create yourself first.
I know Matlab has the function TrainAutoencoder(input, settings) to create and train an autoencoder. The result is capable of running the two functions of "Encode" and "Decode".
But this is only applicable to the case of normal autoencoders. What if you want to have a denoising autoencoder? I searched and found some sample codes, where they used the "Network" function to convert the autoencoder to a normal network and then Train(network, noisyInput, smoothOutput)like a denoising autoencoder.
But there are multiple missing parts:
How to use this new network object to "encode" new data points? it doesn't support the encode().
How to get the "latent" variables to the features, out of this "network'?
I appreciate if anyone could help me resolve this issue.
Thanks,
-Moein
At present (2019a), MATALAB does not permit users to add layers manually in autoencoder. If you want to build up your own, you will have start from the scratch by using layers provided by MATLAB;
In order to to use TrainNetwork(...) to train your model, you will have you find out a way to insert your data into an object called imDatastore. The difficulty for autoencoder's data is that there is NO label, which is required by imDatastore, hence you will have to find out a smart way to avoid it--essentially you are to deal with a so-called OCC (One Class Classification) problem.
https://www.mathworks.com/help/matlab/ref/matlab.io.datastore.imagedatastore.html
Use activations(...) to dump outputs from intermediate (hidden) layers
https://www.mathworks.com/help/deeplearning/ref/activations.html?searchHighlight=activations&s_tid=doc_srchtitle
I swang between using MATLAB and Python (Keras) for deep learning for a couple of weeks, eventually I chose the latter, albeit I am a long-term and loyal user to MATLAB and a rookie to Python. My two cents are that there are too many restrictions in the former regarding deep learning.
Good luck.:-)
If you 'simulation' means prediction/inference, simply use activations(...) to dump outputs from any intermediate (hidden) layers as I mentioned earlier so that you can check them.
Another way is that you construct an identical network but with the encoding part only, copy your trained parameters into it, and feed your simulated signals.
I need to write a program where part of the TensorFlow nodes need to keep being there storing some global information(mainly variables and summaries) while the other part need to be changed/reorganized as program runs.
The way I do now is to reconstruct the whole graph in every iteration. But then, I have to store and load those information manually from/to checkpoint files or numpy arrays in every iteration, which makes my code really messy and error prone.
I wonder if there is a way to remove/modify part of my computation graph instead of reset the whole graph?
Changing the structure of TensorFlow graphs isn't really possible. Specifically, there isn't a clean way to remove nodes from a graph, so removing a subgraph and adding another isn't practical. (I've tried this, and it involves surgery on the internals. Ultimately, it's way more effort than it's worth, and you're asking for maintenance headaches.)
There are some workarounds.
Your reconstruction is one of them. You seem to have a pretty good handle on this method, so I won't harp on it, but for the benefit of anyone else who stumbles upon this, a very similar method is a filtered deep copy of the graph. That is, you iterate over the elements and add them in, predicated on some condition. This is most viable if the graph was given to you (i.e., you don't have the functions that built it in the first place) or if the changes are fairly minor. You still pay the price of rebuilding the graph, but sometimes loading and storing can be transparent. Given your scenario, though, this probably isn't a good match.
Another option is to recast the problem as a superset of all possible graphs you're trying to evaluate and rely on dataflow behavior. In other words, build a graph which includes every type of input you're feeding it and only ask for the outputs you need. Good signs this might work are: your network is parametric (perhaps you're just increasing/decreasing widths or layers), the changes are minor (maybe including/excluding inputs), and your operations can handle variable inputs (reductions across a dimension, for instance). In your case, if you have only a small, finite number of tree structures, this could work well. You'll probably just need to add some aggregation or renormalization for your global information.
A third option is to treat the networks as physically split. So instead of thinking of one network with mutable components, treat the boundaries between fixed and changing pieces are inputs and outputs of two separate networks. This does make some things harder: for instance, backprop across both is now ugly (which it sounds like might be a problem for you). But if you can avoid that, then two networks can work pretty well. It ends up feeling a lot like dealing with a separate pretraining phase, which you many already be comfortable with.
Most of these workarounds have a fairly narrow range of problems that they work for, so they might not help in your case. That said, you don't have to go all-or-nothing. If partially splitting the network or creating a supergraph for just some changes works, then it might be that you only have to worry about save/restore for a few cases, which may ease your troubles.
Hope this helps!
I have a picture.1200*1175 pixel.I want to train a net(mlp or hopfield) to learn a specific part of it(201*111pixel) to save its weight to use in a new net(with the same previous feature)only without train it to find that specific part.now there are this questions :what kind of nets is useful;mlp or hopfield,if mlp;the number of hidden layers;the trainlm function is unuseful because "out of memory" error.I convert the picture to a binary image,is it useful?
What exactly do you need the solution to do? Find an object with an image (like "Where's Waldo"?). Will the target object always be the same size and orientation? Might it look different because of lighting changes?
If you just need to find a fixed pattern of pixels within a larger image, I suggest using a straightforward correlation measure, such as crosscorrelation to find it efficiently.
If you need to contend with any of the issues mentioned above, then there are two basic solutions: 1. Build a model using examples of the object in different poses, scalings, etc. so that the model will recognize any of them, or 2. Develop a way to normalize the patch of pixels being examined, to minimize the effect of those distortions (like Hu's invariant moments). If nothing else, yuo'll want to perform some sort of data reduction to get the number of inputs down. Technically, you could also try a model which is invariant to rotations, etc., but I don't know how well those work. I suspect that they are more tempermental than traditional approaches.
I found AdaBoost to be helpful in picking out only important bits of an image. That, and resizing the image to something very tiny (like 40x30) using a Gaussian filter will speed it up and put weight on more of an area of the photo rather than on a tiny insignificant pixel.