Bckground subraction using k means

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vetri L
vetri L 2019-2-18
评论： vetri L 2019-2-22
I have tried background subtraction algorithm using k means with matlab. Step 4 and 5 are difficult to understand. kinldly check and do the needful
1: Idown ←Down-sample the image to 25% of its original size using
simple linear interpolation.
clc;
clear;
close all;
I = imread ('45.jpg');
Idown=imresize(I,[256,256]);
2: Get the S channel of Idown and consider it as an 1-d vector V of
pixel intensities.
I_hsv = rgb2hsv(Idown);
HSV_s = I_hsv(:,:,2);
HSV_s_1 = HSV_s(:);
3: Perform Dbin ←K-Means(V, k = 2).
Dbin = kmeans(HSV_s_1,2);
4: MapM ← Dbin back to image space. For that just do a linear scan
of Dbin.
please help this step ( how to do linear scan?)
5: Mup ←Up-sample the generated binary mapMback to the input
image size.
please help this step
6: Close small holes on Mup using the Closing morphological operator
with a disk structuring element of radius 7 pixels.
se = strel('disk', 7)
closeBW = imclose(Mup,se);
**********************************************************
Please check the above steps and do the needful

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采纳的回答

Image Analyst
Image Analyst 2019-2-18
Since the color of the objects is nearly the same color as the background, I would probably not use color segmentation or kmeans. I'd probably use stdfilt() to identify "rough" things. The background will be smooth so you can then threshold it away.
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Image Analyst
Image Analyst 2019-2-22
Attached is a start. try playing with the parameters to see if you can optimize it.
vetri L
vetri L 2019-2-22
Thank you sir. I will try with your code

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