Edge detection in MATLAB - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read an image file named 'image.jpg'.

MATLAB

img = imread([1]);

Drag options to blanks, or click blank then click option'

A'image.jpg'

Bimage.jpg

C'img.jpg'

Dimg.jpg

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting the single quotes around the filename.

Using a variable name instead of a string.

The imread function requires the filename as a string, so it must be enclosed in single quotes.

2fill in blank

medium

Complete the code to convert the image to grayscale.

MATLAB

gray_img = [1](img);

Drag options to blanks, or click blank then click option'

Agray2rgb

Brgb2gray

Cim2bw

Dimread

Attempts:

3 left

💡 Hint

Common Mistakes

Using im2bw which converts to binary, not grayscale.

Using imread again instead of conversion.

The function rgb2gray converts a color image to grayscale.

3fill in blank

hard

Fix the error in the code to detect edges using the Canny method.

MATLAB

edges = edge(gray_img, '[1]');

Drag options to blanks, or click blank then click option'

Aprewitt

Blaplacian

Ccanny

Dsobel

Attempts:

3 left

💡 Hint

Common Mistakes

Not putting the method name in quotes.

Using a method name that is not supported.

The edge function uses the method name as a string. For Canny edges, use 'canny'.

4fill in blank

hard

Fill both blanks to display the original and edge images side by side.

MATLAB

subplot(1, 2, [1]);
imshow([2]);

Drag options to blanks, or click blank then click option'

A1

B2

Cimg

Dedges

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping subplot positions.

Showing edges instead of the original image in the first subplot.

The first subplot shows the original image at position 1.

5fill in blank

hard

Fill all three blanks to display the edge image in the second subplot with a title.

MATLAB

subplot(1, 2, [1]);
imshow([2]);
title('[3]');

Drag options to blanks, or click blank then click option'

A1

Bedges

CDetected Edges

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong subplot index.

Not matching the title text exactly.

The second subplot index is 2, showing the edge image with the title 'Detected Edges'.

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