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Max Neudecker

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Company/University
MPIDS Göttingen

Personal Profile:

PhD Student at MPI Göttingen

Professional Interests:
Image processing 3D, Segmentation of CT data

 

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Comments and Ratings by Max View all
Updated File Comments Rating
29 Sep 2013 Export Voxel Data Export objects described in a logical matrix, label matrix or voxel list to STL and PovRay mesh2. Author: Cyprian Lewandowski

Overall, this is a very useful package, which an exceptionally good documentation (See PDF file!) Its main advantage are generic, well accessible interface and the modular, integrated workflow (see the Fig.2 in the manual).

For me (MATLAB 2012b, ubuntu64bit), it works out of the box, and automatically compiled the missing mex files for your platform.

However, I encountered a few Out-Of-Memory issues with the "smoothing" option. If you set the default value,
p.smoothing.smooth = false; (ExportVoxelData.m, line 137) then it works. The "geometric" option took very long, I suppose it got stuck in CONVERT_meshformat.

I think, if you have 3D-data e.g. from X-Ray-Tomography or confocal microscopy, and are looking for nice visualization -even with stereoscopic images- of segmented objects in Povray or with your favourite STL program, this might be the right tool.

Keep up the good work!

05 Jul 2013 FMINLBFGS: Fast Limited Memory Optimizer Quasi newton limited memory BFGS and Steepest decent optimizer for large amount of unknowns Author: Dirk-Jan Kroon

Thanks for providing this useful piece of numerical optimization code. I implemented it for fitting tomographic data to a model. I found that the step sizes for computing the gradient (DiffMinChange) seemed quite crucial for the convergence. Since my set of variables for optimization has different domains (e.g. [0...0.5] or [0....2*PI] ...), I had to modify the code so to account for different DiffMin/MaxChanges.
BTW, is there a fast MEX-version of the (L)BFGS optimizer?

04 Jun 2013 Bradley local image thresholding A fast an well performing local image thresholding method. Author: Jan Motl

This method works quite well for binarizing my samples!
Note: I found that, depending on the grayvalue variations, a lot of background pixels are wrongly segmented as foreground. However, masking with another binary image, binarized e.g. with Otsu's threshold (function graythresh), remedied this.

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