Arduino Computer Vision Programming by Özen Özkaya & Giray Yıllıkçı

Author:Özen Özkaya & Giray Yıllıkçı [Özkaya, Özen]
Language: eng
Format: azw3
Publisher: Packt Publishing
Published: 2015-08-28T04:00:00+00:00

You can see that we have got what we expected! The console output for the empty table is:

mean: 0 stddev: 0 Table is empty!

And console output for the table with different objects is:

mean: 24.2457 stddev: 74.7984 Table is not empty!

As you can see, we represented the whole scene image with two double numbers—mean, and standard deviation. It is possible to develop a table-top object existence detector just by using the mean information. Here the mean value which we used is the extracted feature from the image and it is possible to describe the high-level existence information by using this feature instead of the whole image data. Now, let's examine the following code:

for (int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 ) { GaussianBlur( org_image, blurred_image, Size( i, i ), 0, 0 ); }

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