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#### matlab Question #12

```Implement the statistical edge detection filter [1] (attached) in MATLAB and
choose a 7×7 mask and implement the F and T tests. Then present a range of
images to the filter and display the filtered images. Histogram equalization
should be applied to the implementation. Different masks should be use to
compare the outcome.
• Implement non-maximal suppression in the edge detection to sharpen up the
edge.
• Implement a different size of mask and also compare the performance of this
algorithm with the Canny edge detector on the same images, drawing conclusions
about their relative performance on noisy, clean, textured and cluttered images.

A A 0 B B
A A 0 B B
A A P B B
A A 0 B B
A A 0 B B

0 B B B B
A 0 B B B
A A P B B
A A A 0 B
A A A A 0

Figures 1and 2 are examples of a 90o 135o masks, where the two regions for each
masks are denoted by A and B, the pixel of interest is denoted by P and the 0’s
denoting the separating line between the two masks (these pixels are  ignored during the calculations). You should generate other masks for 0o, 45o etc.
Once you have generated these masks within MATLAB, you need to scan the masks
through the image and read the pixels by its two areas and then calculate statistical
parameters (standard deviations and variations) of regions A and B using the var ( )
function in MATLAB.
The next step will be implementing the two statistical techniques that are used to
determine the edge magnitude and direction for each region:
F-test:
T-test:

```
 0
4/16/2012 9:04:07 PM
comp.soft-sys.matlab 211264 articles. 25 followers. lunamoonmoon (257) is leader.

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```On Tuesday, April 17, 2012 9:04:07 AM UTC+12, angel  wrote:
> Implement the statistical edge detection filter [1] (attached) in MATLAB =
and
> choose a 7=D77 mask and implement the F and T tests. Then present a range=
of
> images to the filter and display the filtered images. Histogram equalizat=
ion
> should be applied to the implementation. Different masks should be use to
> compare the outcome.
> =95 Implement non-maximal suppression in the edge detection to sharpen up=
the
> edge.
> =95 Implement a different size of mask and also compare the performance o=
f this
> algorithm with the Canny edge detector on the same images, drawing conclu=
sions
> about their relative performance on noisy, clean, textured and cluttered =
images.
>=20
>=20
> A A 0 B B                                                  =20
> A A 0 B B
> A A P B B
> A A 0 B B
> A A 0 B B
>=20
>=20
> 0 B B B B
> A 0 B B B
> A A P B B
> A A A 0 B
> A A A A 0
>=20
> Figures 1and 2 are examples of a 90o 135o masks, where the two regions fo=
r each
> masks are denoted by A and B, the pixel of interest is denoted by P and t=
he 0=92s
> denoting the separating line between the two masks (these pixels are  ign=
ored during the calculations). You should generate other masks for 0o, 45o =
etc.
> Once you have generated these masks within MATLAB, you need to scan the m=
> through the image and read the pixels by its two areas and then calculate=
statistical
> parameters (standard deviations and variations) of regions A and B using =
the var ( )
> function in MATLAB.
> The next step will be implementing the two statistical techniques that ar=
e used to
> determine the edge magnitude and direction for each region:
> F-test:
> T-test:

OK, so you've given us your homework assignment, what next?
Do you expect us to do your homework for you?
```
 0
mulgor (3008)
4/16/2012 9:14:00 PM
```TideMan <mulgor@gmail.com> wrote in message <27324771.811.1334610840164.JavaMail.geo-discussion-forums@pbcuu8>...
> On Tuesday, April 17, 2012 9:04:07 AM UTC+12, angel  wrote:
> > Implement the statistical edge detection filter [1] (attached) in MATLAB and
> > choose a 7×7 mask and implement the F and T tests. Then present a range of
> > images to the filter and display the filtered images. Histogram equalization
> > should be applied to the implementation. Different masks should be use to
> > compare the outcome.
> > • Implement non-maximal suppression in the edge detection to sharpen up the
> > edge.
> > • Implement a different size of mask and also compare the performance of this
> > algorithm with the Canny edge detector on the same images, drawing conclusions
> > about their relative performance on noisy, clean, textured and cluttered images.
> >
> >
> > A A 0 B B
> > A A 0 B B
> > A A P B B
> > A A 0 B B
> > A A 0 B B
> >
> >
> > 0 B B B B
> > A 0 B B B
> > A A P B B
> > A A A 0 B
> > A A A A 0
> >
> > Figures 1and 2 are examples of a 90o 135o masks, where the two regions for each
> > masks are denoted by A and B, the pixel of interest is denoted by P and the 0’s
> > denoting the separating line between the two masks (these pixels are  ignored during the calculations). You should generate other masks for 0o, 45o etc.
> > Once you have generated these masks within MATLAB, you need to scan the masks
> > through the image and read the pixels by its two areas and then calculate statistical
> > parameters (standard deviations and variations) of regions A and B using the var ( )
> > function in MATLAB.
> > The next step will be implementing the two statistical techniques that are used to
> > determine the edge magnitude and direction for each region:
> > F-test:
> > T-test:
>
> OK, so you've given us your homework assignment, what next?
> Do you expect us to do your homework for you?

No i need hint or How to start because i dont konw How to srart specially How to make a 90o 135o masks thats what i want
```
 0
4/17/2012 3:20:06 PM
```We can't see the mask examples that he's asking you to use.  But to
make a mask you make up an array of logical (or integer) values

Change some values in there if you want both foreground and background
pixels.
```
 0
imageanalyst (7621)
4/17/2012 9:09:13 PM

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question about design fir filter with matlab
hi all, i need to design fir filter to eliminate frequency component; my signal banwith B=10kHZ, samples Fs=20kHz signal is corrupted by four sinusoids with frequncies: F1=10kHz F2=8889Hz F3=7778Hz F4=6667Hz i found H(z)( H(z)=(z-0.5)*(z-0.3889)*(z-0.4445)*(z-0.3334)) but i don know how can i show in the matlab... pls help me thanx "icestone" <kiresun@hotmail.com> wrote in news:f- 2dneGA8q_b2NbYnZ2dnUVZ_tCdnZ2d@giganews.com: > hi all, > > i need to design fir filter to eliminate frequency component; > my signal banwith B=10kHZ, samples Fs=20kHz > > signal is corrupted by four sinusoids with frequncies: > F1=10kHz F2=8889Hz F3=7778Hz F4=6667Hz > > i found H(z)( H(z)=(z-0.5)*(z-0.3889)*(z-0.4445)*(z-0.3334)) but i dont > know how can i show in the matlab... pls help me > thanx > > > I don't know if you're trying to do this practically or as an exercise, but my opinion is that your sampling rate is not high enough (practically speaking) for your bandwidth. You also didn't state what frequencies you are trying to attenuate., -- Scott Reverse name to reply icestone wrote: > hi all, > > i need to design fir filter to eliminate frequency component; > my signal banwith B=10kHZ, samples Fs=20kHz > > signal is corrupted by four sinusoids with frequncies: > F1=10kHz F2=8889Hz F3=7778Hz F4=6667Hz > > i found H(z)( H(z)=(z-0.5)*(z-0.3889)*(z-0.4445)*(z-0.3334...

Re: dll, matlab an LadVIEW #12
I am trying to get a MATLAB compiled DLL to work in LabVIEW.&nbsp; I have monitored many of the discussion but I don't see anyone having the same problem I am that was resolved.&nbsp; The problem appears to be when the &lt;library&gt; terminate is called along with the MCR terminate function.&nbsp; It doesn't really seem to terminate.&nbsp; Her is a snippet of my code.&nbsp; I have broken the wrapper into 3 parts:&nbsp; Init, Calculate and Uninit.&nbsp;&nbsp;&nbsp; Then in LV I have 3 calls to the call library node.&nbsp; First the init, then a loop to call calculate (vilineWidth) and finally terminate when stop is clicked.&nbsp; If I start fresh - delete the directory created by the MCRInitialize (\cviDLL_mcr) I can successfully call the Init and calculate as many times as I want.&nbsp; When I click stop it "appears" to stop.&nbsp; Problem is that the &lt;library&gt;Terminate does not return a value.&nbsp; If I try to re-run then I get an error when the mclInitializeApplicationfunction is called.&nbsp; If I exit LV and manually blow away the _mcr directory and re-run I can get back to being able to call calculate.&nbsp; If I try to blow away the directory WITHOUT closing LV is says a file is in use and cannot do it.&nbsp; This would seem to suggest that the Terminates are not functioning properly.&nbsp; Any thougts?? &nbsp; LLCVICALC_API int CVIMatlabInit(void) { ...

Incompatible of MAT file for Matlab 6.5.1 and Matlab 7.0
Hi! I have recently installed Matlab 7.0. I have save my simulation results in .MAT file. However, these MAT files can't be opened using Matlab 6.5.1. I encountered problem as follows: ??? Error using ==> load Unable to read MAT file D:\MATLAB7\work\results.mat File may be corrupt. How can I solve this problem? Thanks. Linda Please see here: <http://tx.technion.ac.il/~perryb/matlab7/mat7.pdf> In short, save again as: The new features and enhancements are described in the "Release notes" of Matlab. A new feature that causes incompatibility with previous versions of Matlab is the new encoding of MAT files. Matlab release 14 writes character and figure data to MAT-files using Unicode encoding by default. Unicode encoded MAT-files are not readable by earlier versions of Matlab. If you intend to load your MAT-files created with Matlab release 14, you must override the Unicode default during the save. You can override the default encoding by using the -v6 switch with save and hgsave: save filename -v6 hgsave filename -v6 Hope it helps Linda wrote: > > > Hi! > > I have recently installed Matlab 7.0. > I have save my simulation results in .MAT file. > However, these MAT files can't be opened using Matlab 6.5.1. > > I encountered problem as follows: > > ??? Error using ==> load > Unable to read MAT file D:\MATLAB7\work\results.mat > > File ma...

Recent soft computing papers and MATLAB Toolboxes
Dear Colleagues, I would like to call your kind attention to the updated website of the Soft Computing Research Group at the University of Veszprem (Hungary) http://www.fmt.vein.hu/softcomp/ You can download MATLAB Toolboxes: - Fuzzy Clustering MATLAB Toolbox - Genetic Programming MATLAB Toolbox - Interactive Evolutionary Strategy (EASy) MATLAB Toolbox - Constrained Fuzzy Model Identification for the FMID Toolbox independent MATLAB programs related to: - Data mining * Fuzzy clustering based time-series segmentation * Supervised Fuzzy Clustering for the Identification of Fuzzy Classifiers * Fuzzy Modeling with Multidimensional Membership Functions: Grey-Box Identification and Control Design * Compact TS-Fuzzy Models through Clustering and OLS plus FIS Model Reduction * Inconsistency Analysis of Labeled Data * Star plots - MATLAB files for Graphical Representation of trace elements of clinkers - Process control and monitoring * Feedback Linearizing Control Using Hybrid Neural Networks Identified by Sensitivity Approach * Incorporating Prior Knowledge in Cubic Spline Approximation - Application to the Identification of Reaction Kinetic Models * Identification and Control of Nonlinear Systems Using Fuzzy Hammerstein Models - A Simple Fuzzy Classifier based on manuscripts in PDF about - fuzzy model based process control and monitoring - fuzzy clustering and classification - incorpor...

Please I need a help, how to solve this question in MATLAB?
1. y = 10x / x.^2 + 5x + 8 2. y = 2cos2x+3sinx.^2 ...

Recent soft computing papers and MATLAB Toolboxes
Dear Colleagues, I would like to call your kind attention to the updated website of the Soft Computing Research Group at the University of Veszprem (Hungary) http://www.fmt.vein.hu/softcomp/ You can download MATLAB Toolboxes: - Fuzzy Clustering MATLAB Toolbox - Genetic Programming MATLAB Toolbox - Interactive Evolutionary Strategy (EASy) MATLAB Toolbox - Constrained Fuzzy Model Identification for the FMID Toolbox independent MATLAB programs related to: - Data mining * Fuzzy clustering based time-series segmentation * Supervised Fuzzy Clustering for the Identification of Fuzzy Classifiers * Fuzzy Modeling with Multidimensional Membership Functions: Grey-Box Identification and Control Design * Compact TS-Fuzzy Models through Clustering and OLS plus FIS Model Reduction * Inconsistency Analysis of Labeled Data * Star plots - MATLAB files for Graphical Representation of trace elements of clinkers - Process control and monitoring * Feedback Linearizing Control Using Hybrid Neural Networks Identified by Sensitivity Approach * Incorporating Prior Knowledge in Cubic Spline Approximation - Application to the Identification of Reaction Kinetic Models * Identification and Control of Nonlinear Systems Using Fuzzy Hammerstein Models - A Simple Fuzzy Classifier based on manuscripts in PDF about - fuzzy model based process control and monitoring - fuzzy clustering and classification - incorporation of a priori knowledge in the identif...

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