Edge detection in MATLAB - Mini Project: Build & Apply

Edge detection

📖 Scenario: You are working with images and want to find the edges in a simple black and white picture. Edges help us see where objects start and end, like the outline of a tree or a building.

🎯 Goal: Build a MATLAB program that loads a grayscale image, sets a threshold for detecting edges, applies a simple edge detection method, and then shows the edges found.

📋 What You'll Learn

Load a grayscale image into a variable called img

Create a threshold variable called threshold with value 0.1

Use the edge function with method 'Sobel' and the threshold variable

Display the resulting edge image using imshow

💡 Why This Matters

🌍 Real World

Edge detection helps in photo editing, object recognition, and robot vision by finding outlines in pictures.

💼 Career

Understanding edge detection is useful for jobs in image processing, computer vision, and machine learning.

1

Load the grayscale image

Write a line of code to load the built-in MATLAB image cameraman.tif into a variable called img using the imread function.

MATLAB

img = imread('cameraman.tif'); % Your code here

Hint

The imread function reads an image file into MATLAB.

2

Set the edge detection threshold

Create a variable called threshold and set it to 0.1.

MATLAB

img = imread('cameraman.tif');
threshold = 0.1; % Your code here

Hint

The threshold helps decide which changes in brightness count as edges.

3

Detect edges using the Sobel method

Use the edge function with variables img and threshold, and method 'Sobel'. Store the result in a variable called edges.

MATLAB

img = imread('cameraman.tif');
threshold = 0.1;
edges = edge(img, 'Sobel', threshold); % Your code here

Hint

The edge function finds edges in the image using the method and threshold you give.

4

Show the edge image

Use imshow to display the variable edges.

MATLAB

img = imread('cameraman.tif');
threshold = 0.1;
edges = edge(img, 'Sobel', threshold);
imshow(edges); % Your code here

Hint

The imshow function shows images or logical matrices as pictures.

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