LabVIEW

cancel
Showing results for 
Search instead for 
Did you mean: 

The output of IMAQ correlate is completely wrong

Hey Evan D.

 

I am using the IMAQ Correlate VI in the LABVIEW Student Edition 2009.

and i have a 'little' two questions for you:

1> The above mentioned VI outputs an image scaled from 0 till 255, depending on the degree of matching; my question is: how did they obtain these values,

i used the formulas attached and i obtained values from -1 to 1.

2>where bugs fixed in IMAQ Correlate VI (bugs mentioned by Root Canal ) in LABVIEW 2010 Student Edition.

 

Best Regards

M. Sleiman

Electrical Engineer

YL-Engineering

 

 

0 Kudos
Message 11 of 19
(1,304 Views)

1.  The output is just rescaled from [-1, 1] to [0, 255]

 

2.  The bugs seem to be fixed.  A direct method and the IMAQ Correlate produce very similar but not exact results now.

Randall Pursley
0 Kudos
Message 12 of 19
(1,296 Views)

Thanks Randall

0 Kudos
Message 13 of 19
(1,289 Views)

Here's my VI's....

thanks RC.

 

M.Sleiman

Download All
0 Kudos
Message 14 of 19
(1,268 Views)

I would debate whether this VI has been fixed yet or not.  I am enclosing a VI where I take an array and a kernal, convolve them using the Convolution vi, then convert the array to an IMAQ and again convolve with the kernal but using the IMAQ Convolute vi, yielding a notably different result.  I would be interested to hear if there is a bug in my code or if it's still in the IMAQ Convolute vi.

 

Teresa

0 Kudos
Message 15 of 19
(1,077 Views)

Just noticed this threat was about correlate and not convolute.  I suspect there is a problem with the 2d convolution IMAQ vi though.

0 Kudos
Message 16 of 19
(1,073 Views)

Hi tgmiller,

 

What versions of LabVIEW, Vision Acquisition Software, and Vision Development Module are you using?

Applications Engineer
National Instruments
0 Kudos
Message 17 of 19
(1,044 Views)

I'm using Labview 2010 SP1, Vision Dev 2010 SP1 and, Vsion Acq August 2010.  

 

My coworker believes the problem is likely to be something to do with how the borders are treated by each convolution.  If that is the case then I wish there were better documentation on these two functions because I really think that a 2D conv that takes the same inputs and returns an output of the same sizes should give the same result unless clearly stated why it will not in the documentation.  

 

Thanks,

Teresa

0 Kudos
Message 18 of 19
(1,036 Views)

Thank you for providing that information. I will file a bug request for this and forward it to our R&D team.

Applications Engineer
National Instruments
0 Kudos
Message 19 of 19
(1,019 Views)