To perform a sliding neighborhood operation select a single pixel.
Sliding window image processing matlab.
Nearly always an odd size 31 or 33 is used because then there are the same number of pixels to the left and right the window is centered over the pixel.
B nlfilter a m n fun applies the function fun to each m by n sliding block of the grayscale image a.
Sliding window is a powerful tool that allows to analyze a signal or an image.
Regarding the time window i still think about an idea like a framework on how to implement that in matlab sorry for being such a newbie.
Set gcf name demo by imageanalyst numbertitle off block process the image.
When i tried to describe to my girlfriend what i was doing i explained is like reading a book with a magnifier the magnifier has a defined dimension and you analyze the text the basic way on matlab not the most efficient to do that is 1.
If the window does not meet our desired window size ignore it if window shape 0 winh or window shape 1 winw.
Loop over the sliding window for each layer of the pyramid for x y window in sliding window resized stepsize 32 windowsize winw winh.
Loop over the image pyramid for resized in pyramid image scale 1 5.
A sliding neighborhood operation processes an image one pixel at a time by applying an algorithm to each pixels neighborhood.
Output image will be smaller by a factor of windowsize.
I am struggling a bit with the implementation of a code that works during real time recordings.
The neighborhoods and blocks are then reassembled to form the output image.
Set gcf position get 0 screensize.
B nlfilter a indexed processes a as an indexed image padding with 0 s if the class of a is uint8 uint16 or logical and padding with 1 s otherwise.
Apply a function to the values of the pixels in the neighborhood.
So i try to explain it as well with real numbers instead of variables.
Determine the pixel s neighborhood.
With an even number the output and input images are shifted by a half pixel.
Find the pixel in the output image whose position corresponds to that of the center pixel in the input image.
You can use conv2 or imfilter to slide a 32 by 32 window across the image by one pixel at a time and get the mean.
Continue this is where you would process your window such as applying a machine learning classifier to classify the contents of the window since we.
This function must return a scalar.
In distinct block processing an image is divided into equally sized blocks without overlap and the algorithm is applied to each distinct block.
