Otsu Thresholding Segmentation of Track Lines for a Camera-Based Line-Following Robot

Project info

• Field of studyRobotics and Mechatronics
• LevelUndergraduate thesis (S1)
• Method typeImage Processing
• Estimated build time± 3 working days
• Toolsgraythresh, imbinarize, bwlabel, regionprops (Image Processing Toolbox)
• PriceUS$22 - US$144

Research goal

Apply Otsu thresholding to the track line image while tuning the otsu threshold and the illumination level to assess processing quality.

Variables

Example result

• 0.463Otsu threshold
• 0.851Effectiveness metric
• 0.992Dice coefficient
• 0.999Pixel accuracy
• 8.926Foreground (%)
• 7Connected objects

The Otsu threshold of 0.463 separates objects from background in Track line image with a Dice coefficient of 0.992 and pixel accuracy of 0.999; the foreground covers 8.9% of the image in 7 components.

Computed from illustrative synthetic data, not from a real study.

About Otsu Thresholding and Segmentation

Otsu thresholding automatically chooses an intensity threshold separating object and background by minimising within-class variance. It is used as a first segmentation step, followed by cleaning and labelling objects.

Steps

1. 1Read the grayscale image and check its histogram
2. 2Compute the threshold with graythresh
3. 3Binarise with imbinarize
4. 4Clean the result (bwareaopen, imfill)
5. 5Label objects with bwlabel and measure with regionprops
6. 6Evaluate segmentation with the Dice coefficient if a reference exists

Assumptions to check

• The image histogram is roughly bimodal (object and background)
• Illumination is uniform; otherwise use adaptive thresholding
• Objects are large relative to noise

Reading and reporting the results

Report the threshold, the number of objects, and the size of segmented objects plus a quality measure such as Dice. Explain the method's limits under uneven illumination.

Example MATLAB code

A short starting script with this project's variable names. The delivered version is adapted to your data and fully commented.

rng(2);
[xx, yy] = meshgrid(1:256);
truth = ((xx-60).^2 + (yy-70).^2 < 25^2) | ((xx-150).^2 + (yy-80).^2 < 30^2) | ((xx-110).^2 + (yy-190).^2 < 35^2);
otsu_threshold = 0.3 * xx / 256;                                          % uneven background gradient
track_line_image = mat2gray(0.25 + 0.4*truth + otsu_threshold + 0.06*randn(256));

level = graythresh(track_line_image);
bw = imbinarize(track_line_image, level);
bw = bwareaopen(bw, 100);
bw = imfill(bw, 'holes');
bwa = imbinarize(track_line_image, adaptthresh(track_line_image, 0.4));                  % adaptive threshold for comparison

dice = @(a, b) 2*nnz(a & b) / (nnz(a) + nnz(b));
fprintf('Otsu level = %.3f\n', level);
fprintf('Dice (Otsu) = %.3f, Dice (adaptive) = %.3f\n', dice(bw, truth), dice(bwa, truth));
L = bwlabel(bw);
stats = regionprops(L, 'Area', 'Centroid');
fprintf('Objects found: %d, areas: %s\n', numel(stats), mat2str([stats.Area]));

figure;
subplot(1,3,1); imshow(track_line_image); title('Image');
subplot(1,3,2); imshow(bw); title('Otsu + cleaning');
subplot(1,3,3); imhist(track_line_image); hold on; xline(level, 'r', 'LineWidth', 1.5);

Packages and prices

Choose how much help you need. Each higher package includes everything in the lower ones. Prices exclude 11% VAT.

• Payment: 50% down payment before work starts, balance after the deliverables are handed over
• Tax: Prices exclude 11% VAT
• Data and academic integrity: Results are computed from your own research data; you write and stand behind your manuscript, we help with the analysis and your understanding of it
• Revisions: Up to 2 revisions are included in the price (Package 3: 3 rounds)

Frequently asked questions