Edge detection in MATLAB - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Sobel edge detection on a simple image

What is the output matrix after applying the Sobel edge detection on the given 3x3 image?

MATLAB

img = [10 10 10; 10 0 10; 10 10 10];
edges = edge(img, 'sobel');
disp(edges);

A [0 0 0; 0 1 0; 0 0 0]

B [0 0 0; 0 0 0; 0 0 0]

C [1 1 1; 1 0 1; 1 1 1]

D [0 1 0; 1 1 1; 0 1 0]

💡 Hint

Remember Sobel detects edges by highlighting intensity changes in horizontal and vertical directions.

The Sobel operator detects edges by calculating gradients. The center pixel is darker, so edges appear around it, resulting in a cross shape of ones.

🧠 Conceptual

intermediate

Understanding the role of threshold in edge detection

What happens if you increase the threshold parameter in the edge function in MATLAB?

A More edges are detected, including weaker ones.

B Fewer edges are detected, only the strongest remain.

C The image becomes blurred before edge detection.

D The edge detection method changes automatically.

💡 Hint

Think about what a threshold does in filtering values.

Increasing the threshold means only stronger edges (higher gradient values) are kept, so fewer edges appear.

🔧 Debug

advanced

Identify the error in this Canny edge detection code

What error will this MATLAB code produce?

MATLAB

img = imread('cameraman.tif');
edges = edge(img, 'canny', 0.5, 2.0);
imshow(edges);

A No error, code runs and shows edges.

B Error: Too many input arguments for edge function.

C Error: Image must be grayscale for edge detection.

D Error: Threshold values must be a two-element vector.

💡 Hint

Check the documentation for the edge function with Canny method: scalar threshold and sigma are valid arguments.

No error; the code runs correctly. For Canny, the third argument is a scalar threshold (MATLAB uses it to derive low/high thresholds), and the fourth is the Gaussian sigma.

📝 Syntax

advanced

Correct syntax for using Prewitt edge detection with custom threshold

Which option shows the correct syntax to apply Prewitt edge detection with a threshold of 0.3?

A edges = edge(img, 'prewitt', threshold=0.3);

B edges = edge(img, 'prewitt', [0.3]);

C edges = edge(img, 'prewitt', 0.3);

D edges = edge(img, 'prewitt', 'Threshold', 0.3);

💡 Hint

Check MATLAB documentation for the edge function's threshold parameter format.

The threshold is passed as the third positional argument as a scalar or two-element vector. Option C is correct syntax.

🚀 Application

expert

Number of edge pixels detected by Roberts operator

Given the binary image below, how many edge pixels will the Roberts operator detect?

img = [
 0 0 0 0 0;
 0 1 1 0 0;
 0 1 1 0 0;
 0 0 0 0 0;
];

Code:

edges = edge(img, 'roberts');
n = nnz(edges);

A 8

B 10

C 4

D 6

💡 Hint

Roberts operator detects edges by looking at diagonal differences between pixels.

The Roberts operator detects edges along diagonals. The 2x2 block of ones creates edges on all four sides diagonally, resulting in 8 edge pixels detected.

Practice

1. What is the primary purpose of the edge function in MATLAB?

easy

A. To detect boundaries or edges in images

B. To convert images from color to grayscale

C. To increase the brightness of an image

D. To resize images to a smaller dimension

Solution

1. Step 1: Understand the function's role

   The edge function is designed to find edges or boundaries in images, which helps highlight shapes.

2. Step 2: Compare with other options

   Options A, B, and C describe different image processing tasks, not edge detection.

3. Final Answer:

   To detect boundaries or edges in images -> Option A

4. Quick Check:

   Edge detection = detect boundaries [OK]

Hint: Edge function finds boundaries in images fast [OK]

Common Mistakes:

• Confusing edge detection with color conversion
• Thinking edge increases brightness
• Mixing edge detection with resizing

2. Which of the following is the correct syntax to detect edges using the Sobel method on a grayscale image I in MATLAB?

easy

A. edges = edge(I, Sobel);

B. edges = edge('Sobel', I);

C. edges = edge(I, 'Sobel');

D. edges = edge(I, "sobel");

Solution

1. Step 1: Recall the edge function syntax

   The correct syntax is edge(image, 'method'), where method is a string like 'Sobel'.

2. Step 2: Check each option

   edges = edge(I, 'Sobel'); matches the correct syntax. edges = edge('Sobel', I); reverses arguments. edges = edge(I, Sobel); misses quotes around 'Sobel'. edges = edge(I, "sobel"); uses double quotes which MATLAB does not accept for method names.

3. Final Answer:

   edges = edge(I, 'Sobel'); -> Option C

4. Quick Check:

   edge(image, 'method') syntax [OK]

Hint: Use single quotes for method names in edge function [OK]

Common Mistakes:

• Swapping argument order
• Omitting quotes around method
• Using double quotes instead of single quotes

3. Given the code below, what will be the size of the output variable BW?

I = imread('cameraman.tif');
BW = edge(I, 'Canny');

medium

A. Smaller size than I, numeric matrix

B. Larger size than I, logical matrix

C. Same size as I, numeric matrix

D. Same size as I, logical matrix

Solution

1. Step 1: Understand input and output of edge function

   The input I is a grayscale image matrix. The edge function returns a logical matrix of the same size indicating edge pixels.

2. Step 2: Confirm output type and size

   The output BW is a logical matrix with the same dimensions as I, marking edges as true.

3. Final Answer:

   Same size as I, logical matrix -> Option D

4. Quick Check:

   edge output = logical matrix same size [OK]

Hint: Edge output matches input size, logical type [OK]

Common Mistakes:

• Expecting numeric instead of logical output
• Assuming output size changes
• Confusing output type with input type

4. Identify the error in the following MATLAB code for edge detection:

RGB = imread('peppers.png');
BW = edge(RGB, 'Canny');
imshow(BW);

medium

A. The image must be converted to grayscale before edge detection

B. The 'Canny' method is not supported for color images

C. imshow cannot display logical images

D. imread cannot read PNG images

Solution

1. Step 1: Check input image type for edge function

   The edge function requires a grayscale image. Here, RGB is a color image.

2. Step 2: Identify missing conversion

   Before calling edge, the image should be converted using rgb2gray. Options B, C, and D are incorrect because 'Canny' works on grayscale, imshow can display logical images, and imread supports PNG.

3. Final Answer:

   The image must be converted to grayscale before edge detection -> Option A

4. Quick Check:

   Convert color to grayscale before edge [OK]

Hint: Always convert RGB to grayscale before edge detection [OK]

Common Mistakes:

• Skipping rgb2gray conversion
• Assuming edge works on color images
• Misunderstanding imshow capabilities

5. You want to detect edges in a noisy grayscale image I using MATLAB. Which approach will best reduce noise impact while detecting edges?

hard

A. Directly use edge(I, 'Sobel') without preprocessing

B. Apply Gaussian smoothing with imgaussfilt before using edge(I, 'Canny')

C. Convert image to binary using imbinarize before edge detection

D. Use edge(I, 'Prewitt') with default parameters

Solution

1. Step 1: Understand noise effect on edge detection

   Noisy images cause false edges. Smoothing reduces noise before edge detection.

2. Step 2: Evaluate options for noise reduction

   Apply Gaussian smoothing with imgaussfilt before using edge(I, 'Canny') applies Gaussian smoothing which blurs noise, improving edge detection with Canny. Options B, C, and D do not reduce noise effectively before edge detection.

3. Final Answer:

   Apply Gaussian smoothing with imgaussfilt before using edge(I, 'Canny') -> Option B

4. Quick Check:

   Smooth noisy image before edge detection [OK]

Hint: Smooth noisy images before edge detection [OK]

Common Mistakes:

• Skipping noise reduction step
• Using edge detection directly on noisy images
• Confusing binarization with noise removal