I am working on palmprint authentication, i have captured the palm images and have done the preprocessing and the ROI extraction.
Now i have to extract features from the ROI such as 'principle lines' and then later use this for matching.
So how do i extract these features and find accuracy of matching using these features? Any suggestion or the code regarding this shall be appreciated.
Captured palm image
ROI of palm
You have to enhance principal edges ; you can use firangi filter which is very good at this job
here is link to get code
http://www.mathworks.com/matlabcentral/fileexchange/24409-hessian-based-frangi-vesselness-filter/content/FrangiFilter2D.m
I have tried it on you image here is results
you can change options to get more refined results .I would like to mention that this is just extraction /enhancement of edges not features .I presume that this is your requirement.If you want feature extraction from your palm image i would suggest use
'local binary pattern'
https://en.wikipedia.org/wiki/Local_binary_patterns
I have 2 shapefiles. One is the contors of an area and the other the spot heights. Both of them has a altitude attribute. In ArcGIS there is a tool called topo to raster were you can use both these features to create a dem. In qgis I have only found tools were you can only use one.
Any Ideas?
There is only the interpolation tool that I know of which will create a DEM. Depending on the resolution you're after, you could BUFFER the spot heights, then MERGE SHAPE FILES and run the interpolation tool on that.
Using the graphical modeler would prevent the buffer layer being created making the process a little tidier (and i'm sure there's a better way using the python console). Hope this helps.
I am trying to align an atlas on a brain section via shape similarity. I converted both images to grayscale and filled them in white like so:
Section:
Atlas:
I tried aligning them by similarity using imregtform. However I get the error "Registration failed because optimization diverged." Is there a value in the optimizer that needs to be changed?
Or is there an easier way to do this in MATLAB?
I do not actually have the Image Processing Toolbox, but you should take a look at some of the help files. For instance, Mathworks has many sections online discussing how you can do image processing, including alignment. This link is somewhat a top level discussion (http://www.mathworks.com/help/images/index.html#spatial-transformation-and-image-registration) and this seems like it might be a great tool for you to do image alignment using the control point alignment technique (http://www.mathworks.com/help/images/point-mapping.html).
I hope this helps point you in the right direction. With out having the toolbox, I can't try the suggested tools.
Unfortunatly, Matlabs image registration only offers linear (geometric) transformations atm. That is, only translation, rotation, scale, and shear is allowed. This is not enough for your images since you have local distortions.
What you need is a non-rigid (elastic) registration. You can find some codes for this in the file-exchange.
I have 42 variables and I have calculated the correlation matrix for them in Matlab. Now I would like to visualize it with a schemaball. Does anyone have any suggestions / experiences how this could be done in Matlab? The following pictures will explain my point better:
In the pictures each parabola between variables would mean the strength of correlation between them. The thicker the line is, the more correlation. I prefer the style of picture 1 more than the style in picture 2 where I have used different colors to highlight the strength of correlation.
Kinda finished I guess.. code can be found here at github.
Documentation is included in the file.
The yellow/magenta color (for positive/negative correlation) is configurable, as well as the fontsize of the labels and the angles at which the labels are plotted, so you can get fancy if you want and not distribute them evenly along the perimeter/group some/...
If you want to actually print these graphs or use them outside matlab, I suggest using vector formats (eg eps). It's also annoying that the text resizes when you zoom in/out, but I don't know of any way to fix that without hacking the zoom function :/
schemaball % demo
schemaball(arrayfun(#num2str,1:10,'uni',false), rand(10).^8,11,[0.1587 0.8750],[0.8333 1],2*pi*sin(linspace(0,pi/2-pi/20,10)))
schemaball(arrayfun(#num2str,1:50,'uni',false), rand(50).^50,9)
I finished and submitted my version to the FEX: schemaball and will update the link asap.
There are a some differences with Gunther Struyf's contribution:
You can return the handles to the graphic object for full manual customization
Labels are oriented to allow maximum left-to-rigth readability
The figure stretches to fit labels in, leaving the axes unchanged
Syntax requires only correlations matrix (but allows optional inputs)
Optimized for performance.
Follow examples of demo, custom labels and creative customization.
Note: the first figure was exported with saveas(), all others with export_fig.
schemaball
x = rand(10).^3;
x(:,3) = 1.3*mean(x,2);
schemaball(x, {'Hi','how','is','your','day?', 'Do','you','like','schemaballs?','NO!!'})
h = schemaball;
set(h.l(~isnan(h.l)), 'LineWidth',1.2)
set(h.s, 'MarkerEdgeColor','red','LineWidth',2,'SizeData',100)
set(h.t, 'EdgeColor','white','LineWidth',1)
The default colormap:
To improve on screen rendering you can launch MATLAB with the experimental -hgVersion 2 switch which produces anti/aliased graphics by default now (source: HG2 update | Undocumented Matlab). However, if you try to save the figure, the file will have the usual old anti-aliased rendering, so here's a printscreen image of Gunther's schemaball:
Important update:
You can do this in Matlab now with the FileExchange submission:
http://www.mathworks.com/matlabcentral/fileexchange/48576-circulargraph
There is an exmample by Matlab in here:
http://uk.mathworks.com/examples/matlab/3859-circular-graph-examples
Which gives this kind of beautiful plots:
Coincidentally, Cleve Moler (MathWorks Chief Mathematician) showed an example of just this sort of plot on his most recent blog post (not nearly as beautiful as the ones in your example, and the connecting lines are straight rather than parabolic, but it looks functional). Unfortunately he didn't include the code directly, but if you leave him a comment on the post he's usually very willing to share things.
What might be even nicer for you is that he also applies (and this time includes) code to permute the rows/columns of the array in order to maximize the spatial proximity of highly connected nodes, rather than randomly ordering them around the circumference. You end up with a 'crescent'-shaped envelope of connecting lines, with the thick bit of the crescent representing the most highly connected nodes.
Unfortunately however, I suspect that if you need to enhance his code to get the very narrow, high-resolution lines in your example plots, then MATLAB's currently non-anti-aliased graphics aren't quite up to it yet.
I've recently been experimenting with MATLAB data and the D3 visualization library for similar graphs - there are several related types of circular visualizations you may be interested in and many of them are interactive. Another helpful, well-baked, and freely available option is Circos which is probably responsible for most of the prettier versions of these graphs you've seen in popular press.
I'm a newbie to Matlab. I'm basically attempting to manually segment a set of images and then manually label those segments also. I looked into the imfreehand(), but I'm unable to do this using imfreehand().
Basically, I want to follow the following steps :
Manually segment various ROIs on the image (imfreehand only lets me draw one segment I think?)
Assign labels to all those segments
Save the segments and corresponding labels to be used further (not sure what format they would be stored in, I think imfreehand would give me the position and I could store that along with the labels?)
Hopefully use these labelled segments in the images to form a training dataset for a neural network.
If there is some other tool or software which would help me do this, then any pointers would be very much appreciated. (Also I am new to stackoverflow, so if there is any way I could improve on the question to make it clearer, please let me know!) Thanks!
Derek Hoiem, a computer vision research at the University of Illinois, wrote an object labelling tool which does pretty much exactly what you asked for. You can download it from his page:
http://www.cs.illinois.edu/homes/dhoiem/software/index.html