coordinates of high frequency
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Is there any way to find points or coordinates of high frequencies in a 2d image?
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Abhishek Tiwari
il 26 Giu 2022
Modificato: Abhishek Tiwari
il 26 Giu 2022
Are you referring to points that occur the most frequently or the points with maximum intensity?
Risposte (1)
Rahul
il 10 Set 2024
I understand that you want to find the coordinates of high frequency from a 2d image.
If you wish to find the coordinates of strongest frequency acquired from 2d fft spectrum, you can follow the following method:
- Convert the image to the frequency domain using 'fft2' function.
- Shift frequency components to center at zero frequency using 'fftshift' function.
- Obtain the magnitude spectrum to identify and threshold the highest frequencies.
% Considering 'img' to be the variable for the 2d Image
img = rgb2gray(img);
% Converting 'img' to frequency domain using 'fft2' function
% Shifting the Zero Frequncy component using 'fftshift' function
F = fft2(double(img));
F_shifted = fftshift(F);
% Obtaining the magnitude spectrum of frequencies
magnitude = abs(F_shifted);
magnitude = log(1 + magnitude);
magnitude = mat2gray(magnitude);
threshold = 0.7; % Using a threshold to obtain high frequncies. Can be adjusted according to use-case.
high_freq_mask = magnitude > threshold;
% Finding Coordinates of High Frequencies
[row, col] = find(high_freq_mask);
hold on;
plot(col, row, 'r*');
title('High Frequency Points');
If you wish to find the coordinates of highest frequency based on occurrance, you can follow the following method:
- Identify features from the 2d Image using 'detectSURFFeatures' function.
- Using 'selectStrongest' function from the identified points to extract the points occurring most frequently.
- If required, 'kmeans' function can be leveraged to obtain clusters of high frequnecy points and showcase their centers.
% Considering 'img' to be the variable for the 2d Image
img = rgb2gray(img);
% Detect features
points = detectSURFFeatures(img);
% Extract and plot the strongest points
strongestPoints = points.selectStrongest(100); % 100 can be changed accoridng to use-case
imshow(img); hold on;
plot(strongestPoints);
% Perform clustering using 'kmeans' function and plot centers
coordinates = strongestPoints.Location;
[idx, C] = kmeans(coordinates, 5); % Example with 5 clusters, can be changed accordingly
plot(C(:,1), C(:,2), 'rx', 'MarkerSize', 15, 'LineWidth', 3);
You can refer to the following Mathworks documentations to know more about these functions:
'detectSURFFeatures': https://www.mathworks.com/help/releases/R2024a/vision/ref/detectsurffeatures.html?searchHighlight=detectSURFFeatures&s_tid=doc_srchtitle
'selectStrongest': https://www.mathworks.com/help/releases/R2024a/vision/ref/kazepoints.selectstrongest.html?searchHighlight=selectStrongest&s_tid=doc_srchtitle
Hope this helps!
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