So I am wondering if it is possible to have a 2D grid style input into a conventional LSTM in Keras? For example: a grid representing a room with a 1 wherever a person in standing at every time step.
You can try ConvLSTM, which can be used to implement spatiotemporal model.
Related
I have a question about Matlab. I have a three dimensional model (*.stl) in Matlab and I want to create some kind of a two dimensional projection. The attached image shows the visualisation of the model by using the patch command. I want to create a two dimensional image/plot from this view on the three dimensional model. This two dimensional projection should have an own x-y-coordinate system, is that possible?
3D view
New coordinate system
Can anyone tell me how to generate a 3D surface model like CAD in Matlab ?
1.Input: Input is a collection of points with (x,y,z) where surface is present for an object(I'm using this for a 3D scanner where my inputs are (x,y,z) of surface)
2.Points should be displayed as a surface using some smooth interpolation.
3.More like surface generation from data points.
Thanks you.
In order to plot surfaces, you can use patch function. However, you need along with the points the faces information. In patch a surface consists of polygons that is specified using 3 point, which is the face information.
1
Since it seems like you will be inputting discrete points located on the surface of the object, you will first want to create a Nonconvex Polygon based on the data using Matlab's boundary function.
https://www.mathworks.com/help/matlab/ref/boundary.html
You can then use the trimesh function to create the figure
This question shows the input data and what was produced using this method: How do I create a 3D polygon/mesh over data points?
I want to create a 5 dimensional plotting in matlab. I have two files in my workspace. one is data(150*4). In this file, I have 150 data and each has 4 features. Since I want to classify them, I have another file called "labels" (150*1) that includes a label for each data in data files. In other words the label are the class of data and I have 3 class: 1,2,3
I want to plot this classification, but i can't...
Naris
You need to think about what kind of plot you want to see. 5 dimensions are difficult to visualize, unless of course, your hyper-dimensional monitor is working. Mine never came back from the repair shop. (That should teach me for sending it out.)
Seriously, 5 dimensional data really can be difficult to visualize. The usual solution is to plot points in a 2-d space (the screen coordinates of a figure, for example. This is what plot essentially does.) Then use various attributes of the points plotted to show the other three dimensions. This is what Chernoff faces do for you. If you have the stats toolbox, then it looks like glyphplot will help you out. Or you can plot in 3-d, then use two attributes to show the other two dimensions.
Another idea is to plot points in 2-d to show two of the dimensions, then use color to indicate the other three dimensions. Thus, the RGB assigned to that marker will be defined by the other three dimensions. Of course, that means you must be able to visualize what the RGB coordinates of a color represent, so you need to understand color as it is represented in an RGB space.
You can use scatter3 to plot your data, using three features of data as dimensions, the fourth as color, and the class as different markers
figure,hold on
markerList = 'o*+';
for iClass = 1:nClasses
classIdx = dataClass==iClass;
scatter3(data(classIdx,1),data(classIdx,2),data(classIdx,3),[],data(classIdx,4),...
'marker',markerList(iClass));
end
When you use color to represent one of the features, I suggest to use a good colormap, such as pmkmp from the Matlab File Exchange instead of the default jet.
Alternatively, you can use e.g. mdscale to transform your higher-dimensional data to 2D for standard plotting.
There's a model called SOM (Self-organizing Maps) which builds a 2-D image of a multidimensional space.
mI currently possess a triangular mesh model of a heart that is loaded into Matlab for further manipulation. The current problem is the that I require a regular (square) mesh model of the heart in order to perform proper texture mapping of an image. Can anyone provide a function or even another program that can convert the current TRIANGULAR mesh model to a regular SQUARE model? Thank you :)
I've had good results with the FEX submission GridTriMesh.
I am to create a 3D volume out of grayscale image set using Matlab. A set contains a continuous and quantized slices of 2D grayscale image. I am still considered myself a rookie in Matlab, but this is what I currently have in my mind:
create an empty space for 3D volume.
On each image, we perform all the preprocessing operation so that we only got the part that is of our interest. (In this question, assume that this preprocessing part always work flawlessly)
Go through the image, each pixel's x and y coordinate on 2D will be transfer to the empty space. For z coordinate, we can use the slice number with respect to the distance between each slice. If a pixel is adjacent to another pixel, the 3D points will be connected together.
Repeat the previous 2 steps until all slices are done. We will now have all the points connected just like in the 2D slices.
But here comes the trouble, how can we connect the points between the slices, so that these points can become a volume? Or is there a more robust way to do in Matlab? Any suggestion is highly appreciated.
Part 0 - Assumptions
all 2D images are of the same dimension, hence your 3D volume can hold all of them in a rectangular cube
majority of the pixels in each of the 2D images have 3D spatial relationships (you can't visualize much if the pixels in each of the 2D images are of some random distribution. )
Part 1 - Visualizing 3D Volume from A Stack of 2D Images
To visualize or reconstruct a 3D volume from a stack of 2D images, you can try the following toolkits in matlab.
1 3D CT/MRI images interactive sliding viewer
http://www.mathworks.com/matlabcentral/fileexchange/29134-3d-ctmri-images-interactive-sliding-viewer
[2] Viewer3D
http://www.mathworks.com/matlabcentral/fileexchange/21993-viewer3d
[3] Image3
http://www.mathworks.com/matlabcentral/fileexchange/21881-image3
[4] Surface2Volume
http://www.mathworks.com/matlabcentral/fileexchange/8772-surface2volume
[5] SliceOMatic
http://www.mathworks.com/matlabcentral/fileexchange/764
Note that if you are familiar with VTK, you can try this:
[6] matVTK
http://www.cir.meduniwien.ac.at/matvtk/
I am currently sticking with [5] SliceOMatic for its simplicity and ease of use. However, by default, rendering 3D is quite slow in Matlab. Turning on openGL would give faster rendering. (http://www.mathworks.com/help/techdoc/ref/opengl.html) Or simply put, set(gcf, 'Renderer', 'OpenGL').
Part 2 - Interpolating pixels in between the slices
To interpolate pixels in between the slices, you need to specify an interpolation method (some of the above toolkits have this capability / flexibility. Otherwise, to give you a head start, some examples for interpolation are bicubic, spline, polynomial etc..(you can work this out by looking up on google or google/scholar for interpolation methods much more specific to your problem domain).
Part 3 - 3D Pre-processing
Looking at your procedures, you process the volumetric data by processing each of the 2D images first. In many advanced algorithms, or in true 3D processing, what you can do is to process the volumetric data in 3D domain first (simply put, you take the 26 neighbors or more in to account first.). Once this step is done, you can simply output the volumetric data into a stack of 2D images for cross-sectional viewing or supply to one of the aforementioned toolkits for 3D viewing or output to third party 3D viewing applications.
I have followed the above concepts for my own medical imaging research projects and the above finding is based on my research experience documented here (with latest revisions).
MATLAB generally plots volumetric data using a 3d array. The data points are spatially evenly separated along each axis. If there are sites in the 3d array for which you do not have data for, usually they are assigned the NaN value and the various plotting functions can generally handle this in a reasonable way (i.e. will generally behave as you intended).
If you load the slices into the 3d array such that adjacent points in the z-direction of the data are also adjacent in the 3rd dimension of the array then you should be fine.