Image segmentation using bwareafilt

I have set of depth images in which I want to segement an object from it. I was able to get binary image of the object but I also have some unwanted data/noise along with it. Can someone suggest how to remove them?

Thanks in advance.

clc;  
clear all;
close all;
workspace;  

s = load(strcat('test_data/data3/d2/images_depth.mat')); %source of data
for i=1:7
img = (s.images_depth{1,i});
[filledDepth, zeroPixels] = Kinect_DepthNormalization(img);
a = mat2gray(filledDepth);  
b = imbinarize(a);
b = bwareafilt(b, 1);
c = ~(b);
figure, imshow(c) 
% figure,imshow(filledDepth, [])
figure,imshow(img, [])
% figure, imagesc(filledDepth)
end

Answer 1

[colordepth]

To clean up noise in your depth images, consider using a median filter, which is a straightforward method for noise removal. Check out this guide here: https://www.mathworks.com/discovery/denoising.html. Applying this before binarization will help reduce unwanted noise.

For binarizing your depth images, you can manually set a threshold in "imbinarize" by passing a second numerical argument. Experiment with thresholds to find one that best segments your object. More details on "imbinarize" can be found here: https://www.mathworks.com/help/images/ref/imbinarize.html. Rest assured that this will work consistently if your camera and table setup remain unchanged.

If you want an automated approach to automatically adjust to your setup, I suggest programming the following framework:

1. Noise Removal: For every captured depth image, perform noise removal.
2. Calibration Step (One-Time): Capture a depth image of your table without any objects on it. Calculate the mean of the smallest 40,000 pixels, which represents about 20% of the 424x512 resolution. This mean value will serve as your "baseEstimate," providing a good approximation of the table's surface. You can tinker with this percentage if it does not work too well for your task.
3. Object Segmentation Step: With an object on the table, capture a depth image. Calculate an estimate with a similar approach above for the top of the object, referred to as "topEstimate." Use these estimates to determine a threshold for the "imbinarize" function. A threshold like "baseEstimate + 0.1*(topEstimate-baseEstimate)" should effectively separate the object from the table in the binarized image.

I enjoyed tinkering with depth cameras during my undergraduate, I'd be happy to help in answering any questions you have about the above approach :)