Why image processing transforms visual data in MATLAB - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read an image file into MATLAB.

MATLAB

img = imread([1]);

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

Areadimage('image.jpg')

Bimage.jpg

C'image.jpg'

Dload('image.jpg')

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting quotes around the filename

Using incorrect functions like load or readimage

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

Agrayconvert

Bconvert2gray

Cimgray

Drgb2gray

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-existent functions

Misspelling the function name

The function rgb2gray converts a color image to grayscale in MATLAB.

3fill in blank

hard

Fix the error in the code to display the image correctly.

MATLAB

imshow([1]);

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

Agray_img

Bimread

Cimage

Dimg

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the function name instead of variable

Passing the original image instead of processed one

To display the processed grayscale image, use imshow(gray_img).

4fill in blank

hard

Fill both blanks to create a binary image by thresholding the grayscale image.

MATLAB

binary_img = [1](gray_img, [2]);

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

Aimbinarize

B0.5

C1.5

Drgb2gray

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong function names

Using thresholds outside 0-1 range

The imbinarize function converts grayscale images to binary using a threshold like 0.5.

5fill in blank

hard

Fill all three blanks to apply a Gaussian blur filter to the image.

MATLAB

h = fspecial([1], [2]); blurred_img = imfilter([3], h);

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

A'gaussian'

B2

Cimg

D'average'

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong filter types

Passing wrong variables to imfilter

Incorrect filter size

The fspecial function creates a Gaussian filter with size 2, then imfilter applies it to the original image.

Practice

1. Why do we use image processing to transform visual data in MATLAB?

easy

A. To make images easier for computers to analyze

B. To make images look worse

C. To delete images permanently

D. To convert images into text files

Solution

1. Step 1: Understand the purpose of image processing

   Image processing changes images to help computers understand and analyze them better.

2. Step 2: Identify the correct goal

   Making images easier for computers to analyze matches the purpose of image processing.

3. Final Answer:

   To make images easier for computers to analyze -> Option A

4. Quick Check:

   Image processing helps computers understand images = A [OK]

Hint: Image processing helps computers, not just humans [OK]

Common Mistakes:

• Thinking image processing only improves visual quality
• Confusing image processing with file deletion
• Assuming image processing converts images to text

2. Which MATLAB command correctly converts an image to grayscale?

easy

A. imresize(image, 0.5)

B. rgb2gray(image)

C. imrotate(image, 90)

D. imwrite(image, 'file.jpg')

Solution

1. Step 1: Identify the function for grayscale conversion

   The MATLAB function rgb2gray converts color images to grayscale.

2. Step 2: Check other options

   imresize changes size, imrotate rotates, and imwrite saves images, so they are incorrect here.

3. Final Answer:

   rgb2gray(image) -> Option B

4. Quick Check:

   Grayscale conversion uses rgb2gray() = A [OK]

Hint: Grayscale means rgb2gray() in MATLAB [OK]

Common Mistakes:

• Using imresize instead of rgb2gray
• Confusing rotation with color change
• Trying to save instead of convert

3. What is the output size of the image after running this MATLAB code?

img = imread('peppers.png');
small_img = imresize(img, 0.25);
size_small = size(small_img);

medium

A. [384 512 3]

B. [192 256 3]

C. [48 64 3]

D. [96 128 3]

Solution

1. Step 1: Understand original image size

   The 'peppers.png' image is 384x512 pixels with 3 color channels (RGB).

2. Step 2: Calculate new size after resizing by 0.25

   Multiplying height and width by 0.25: 384*0.25=96, 512*0.25=128, channels remain 3.

3. Final Answer:

   [96 128 3] -> Option D

4. Quick Check:

   384*0.25=96 and 512*0.25=128 [OK]

Hint: Multiply original dimensions by resize factor [OK]

Common Mistakes:

• Mixing width and height order
• Forgetting color channels
• Using 0.5 instead of 0.25 scale

4. Find the error in this MATLAB code that tries to increase image brightness:

img = imread('cameraman.tif');
bright_img = img + 50;
imshow(bright_img);

medium

A. imread does not load images

B. imshow cannot display images

C. Adding 50 causes overflow without conversion

D. The variable name bright_img is invalid

Solution

1. Step 1: Check data type of image

   Images loaded by imread are usually uint8, which can overflow when adding numbers.

2. Step 2: Identify overflow issue

   Adding 50 directly can cause values above 255 to wrap around, causing wrong brightness.

3. Final Answer:

   Adding 50 causes overflow without conversion -> Option C

4. Quick Check:

   uint8 + 50 may overflow = C [OK]

Hint: Convert to double before adding brightness [OK]

Common Mistakes:

• Ignoring data type overflow
• Thinking imshow cannot display images
• Assuming variable names cause errors

5. You want to convert a color image to grayscale and then resize it to half its size in MATLAB. Which code correctly does this?

hard

A. gray_img = imresize(rgb2gray(img), 0.5);

B. gray_img = rgb2gray(img); imresize(gray_img, 2);

C. gray_img = imresize(img, 2); gray_img = rgb2gray(gray_img);

D. gray_img = rgb2gray(imresize(img, 0.5));

Solution

1. Step 1: Convert color image to grayscale first

   Use rgb2gray(img) to get grayscale image before resizing.

2. Step 2: Resize the grayscale image to half size

   Use imresize(..., 0.5) on the grayscale image to reduce size by half.

3. Final Answer:

   gray_img = imresize(rgb2gray(img), 0.5); -> Option A

4. Quick Check:

   Convert first, then resize = D [OK]

Hint: Convert color to grayscale before resizing [OK]

Common Mistakes:

• Resizing before converting causes wrong results
• Using scale factor 2 instead of 0.5
• Not assigning resized image to variable