%Here's a MATLAB code that performs Independent Component Analysis (ICA) on a video sequence. The goal is to treat each frame as a data %vector and then extract statistically independent components from this set using ICA.
%This is particularly useful for motion segmentation, source separation, or background subtraction tasks.
% ICA on a video sequence clc; clear; close all;
% Load video videoFile = 'your_video.mp4'; % Change to your video file v = VideoReader(videoFile); numFrames = floor(v.Duration * v.FrameRate);
% Read first frame to get dimensions frame = readFrame(v); grayFrame = rgb2gray(frame); [rows, cols] = size(grayFrame); frameSize = rows * cols;
% Reset video to the beginning v.CurrentTime = 0;
% Construct data matrix: each column is a vectorized frame dataMatrix = zeros(frameSize, numFrames); frameCount = 0;
while hasFrame(v) frame = readFrame(v); grayFrame = rgb2gray(frame); grayFrame = im2double(grayFrame); % Normalize to [0,1] frameCount = frameCount 1; dataMatrix(:, frameCount) = grayFrame(:); end
% Remove mean for ICA X = dataMatrix - mean(dataMatrix, 2);
% Perform ICA using FastICA addpath('fastica'); % Make sure FastICA is in your path (download from https://research.ics.aalto.fi/ica/fastica/) [icasig, A, W] = fastica(X, 'numOfIC', 10); % You can change the number of ICs
% Display some ICs as images figure; for i = 1:9 subplot(3,3,i); imagesc(reshape(icasig(i, :), [1, frameCount])); colormap gray; axis off; title(['IC ', num2str(i)]); end
% You can also reshape the spatial ICs if desired figure; for i = 1:9 subplot(3,3,i); imagesc(reshape(A(:, i), rows, cols)); colormap gray; axis off; title(['Spatial IC ', num2str(i)]); end
disp('ICA completed successfully!');
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Independent Component Analysis (ICA) for a video sequence. ChatGPT
Amardip Ghosh #Advanced Propulsion Systems (APSYS) Lab
%Here's a MATLAB code that performs Independent Component Analysis (ICA) on a video sequence. The goal is to treat each frame as a data %vector and then extract statistically independent components from this set using ICA.
%This is particularly useful for motion segmentation, source separation, or background subtraction tasks.
% ICA on a video sequence
clc; clear; close all;
% Load video
videoFile = 'your_video.mp4'; % Change to your video file
v = VideoReader(videoFile);
numFrames = floor(v.Duration * v.FrameRate);
% Read first frame to get dimensions
frame = readFrame(v);
grayFrame = rgb2gray(frame);
[rows, cols] = size(grayFrame);
frameSize = rows * cols;
% Reset video to the beginning
v.CurrentTime = 0;
% Construct data matrix: each column is a vectorized frame
dataMatrix = zeros(frameSize, numFrames);
frameCount = 0;
while hasFrame(v)
frame = readFrame(v);
grayFrame = rgb2gray(frame);
grayFrame = im2double(grayFrame); % Normalize to [0,1]
frameCount = frameCount 1;
dataMatrix(:, frameCount) = grayFrame(:);
end
% Remove mean for ICA
X = dataMatrix - mean(dataMatrix, 2);
% Perform ICA using FastICA
addpath('fastica'); % Make sure FastICA is in your path (download from https://research.ics.aalto.fi/ica/fastica/)
[icasig, A, W] = fastica(X, 'numOfIC', 10); % You can change the number of ICs
% Display some ICs as images
figure;
for i = 1:9
subplot(3,3,i);
imagesc(reshape(icasig(i, :), [1, frameCount]));
colormap gray; axis off;
title(['IC ', num2str(i)]);
end
% You can also reshape the spatial ICs if desired
figure;
for i = 1:9
subplot(3,3,i);
imagesc(reshape(A(:, i), rows, cols));
colormap gray; axis off;
title(['Spatial IC ', num2str(i)]);
end
disp('ICA completed successfully!');