Rank: 738 based on 185 downloads (last 30 days) and 5 files submitted
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Nobuhito Mori

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Company/University
Kyoto University

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http://www.oceanwave.jp/

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Files Posted by Nobuhito View all
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(last 30 days)
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19 Mar 2014 Despiking This function remove spike noise from data. Author: Nobuhito Mori filter design, filter analysis, spike noise despiking... 80 15
  • 4.16667
4.2 | 6 ratings
17 Nov 2011 Screenshot Trans Camera View This .m function transforms camera captured image to planner plane using perspective transformation. Author: Nobuhito Mori geometric transformat..., image registration, perspective transform... 26 5
  • 2.5
2.5 | 4 ratings
11 Feb 2010 datetick2 This simple script an extension of datetick for plotyy. Author: Nobuhito Mori plot, plotyy, axis, tickmark 16 1
  • 3.0
3.0 | 1 rating
13 Jul 2009 Screenshot mpiv PIV method in MATLAB Author: Nobuhito Mori application, piv fluid experiments 33 15
  • 3.72727
3.7 | 12 ratings
01 Jul 2009 func_coherence.m This m-function computes coherence and phase at the same time. Author: Nobuhito Mori spectral analysis, cross spectrum, coherence, phase, signal processing 30 6
  • 3.5
3.5 | 8 ratings
Comments and Ratings by Nobuhito
Updated File Comments Rating
26 Oct 2012 mpiv PIV method in MATLAB Author: Nobuhito Mori

I think there are several required level of image.
1. enough particle density in the image
2. uniformly distributed particle in the image

If your image is not satisfy above two conditions, 'mpd' is more robust to obtain vectors.
here is sample script to obtain attached results.
[xi,yi,iu,iv] = mpiv(f1,f2, 64,64, 0.5,0.5, 20,20, 1, 'mqd', 2, 1);
[iu_f,iv_f,iu_i, iv_i] = mpiv_filter(iu,iv, 2, 2.0, 3, 1);

Comments and Ratings on Nobuhito's Files View all
Updated File Comment by Comments Rating
17 Oct 2013 Despiking This function remove spike noise from data. Author: Nobuhito Mori Schindfessel, Laurent

Dear Mr. Mori, dear all,

When looking at these scripts, I found a difference with the algorith of Goring and Nikora (2002), and I wonder if there is a certain reason why it is so.

In these scripts, an ellipsoide is constructed with axes lambda*sigma_u , lambda*sigma_du and lambda*sigma_d2u. However, in the article of Goring and Nikora the ellipsoide has axes 'a', lambda*sigma_du and 'b', with 'a' and 'b' defined by their equations 9 and 10. The axes 'a' and 'b' are chosen so that the maxima of the ellipse, i.e. the points with extreme values of u and d2u, are equal to lambda*sigma_u and lambda*sigma_d2u. Note that these maxima points are in general not equal to the main axes.

It is a very subtile but fundamental difference, and I wonder if there is a certain reason why this change has been made?

A good thing about these scripts is that it circumvents a problem with the algorithm of Goring and Nikora: if sigma_u and sigma_d2u substantially differ, the latter algorithm can not define a proper ellipsoide. I face this problem with my ADV data, and that's why I am interested in your method. The algorithm in these scripts can always produce an ellipsoide.

I would like to stress that this alternative in these scripts is not 'erroneous', since it uses a certain logic, just as the algorith of Goring an Nikora uses a logic. Since despiking is not an exact science, it is still open for debate which logic is best.

Only one should be aware that these scripts do not implement the method of Goring and Nikora (2002), but an alternative.

I look forward to your comments and ideas.

Kind regards,
Laurent Schindfessel
Ghent University

13 Aug 2013 Despiking This function remove spike noise from data. Author: Nobuhito Mori Thomas

The despiking process is 3 times faster if lines 100-106 in function "func_excludeoutlier_ellipsoid3d.m"
>> z2 = -sqrt(zt);
elseif z1 > 0
z2 = sqrt(zt);
else
z2 = 0;
end

are replaced by this one-liner:

z2=sign(z1)*sqrt(zt);

Thanks for these functions.

20 Mar 2013 Despiking This function remove spike noise from data. Author: Nobuhito Mori Tanja

I'm trying to use this to only remove (or interpolate over) spikes that are, lets say, >5 above the surrounding data points. Any ideas?

26 Oct 2012 mpiv PIV method in MATLAB Author: Nobuhito Mori Mori, Nobuhito

I think there are several required level of image.
1. enough particle density in the image
2. uniformly distributed particle in the image

If your image is not satisfy above two conditions, 'mpd' is more robust to obtain vectors.
here is sample script to obtain attached results.
[xi,yi,iu,iv] = mpiv(f1,f2, 64,64, 0.5,0.5, 20,20, 1, 'mqd', 2, 1);
[iu_f,iv_f,iu_i, iv_i] = mpiv_filter(iu,iv, 2, 2.0, 3, 1);

25 Oct 2012 mpiv PIV method in MATLAB Author: Nobuhito Mori Ian

Help! I can run the examples bmp with no issues, but with my own ones i get this error.

??? Error using ==> chckxy at 106
There should be at least two data points.

As i have more 'speckle' images i used 'mqd' and the above error presumably came from an abundance of

Warning: Divide by zero.

during processing...and advice?

Thanks!

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