Image thresholding (binary, adaptive, Otsu) in Computer Vision - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is binary image thresholding?

Binary image thresholding converts a grayscale image into a black and white image by choosing a fixed cutoff value. Pixels above the cutoff become white, and pixels below become black.

intermediate

How does adaptive thresholding differ from binary thresholding?

Adaptive thresholding calculates the cutoff value for each small region of the image separately, allowing it to handle varying lighting conditions better than a single global cutoff.

intermediate

What is Otsu's method in image thresholding?

Otsu's method automatically finds the best global threshold by minimizing the variance within pixel groups, separating foreground and background without manual cutoff selection.

beginner

Why is adaptive thresholding useful in real-life images?

Because real-life images often have uneven lighting, adaptive thresholding adjusts the cutoff locally, making it better at separating objects from backgrounds in shadows or bright spots.

beginner

What is a common use case for Otsu's thresholding?

Otsu's thresholding is often used in document scanning to separate text (foreground) from the paper (background) automatically and clearly.

What does binary thresholding do to pixel values?

A Converts pixels to either black or white based on a fixed cutoff

B Adjusts pixel brightness locally

C Blurs the image to reduce noise

D Separates colors into multiple channels

Binary thresholding sets pixels above a fixed cutoff to white and below to black.

Which thresholding method adapts to different lighting in image regions?

A Binary thresholding

B Otsu's thresholding

C Histogram equalization

D Adaptive thresholding

Adaptive thresholding calculates thresholds locally for each region, handling uneven lighting.

Otsu's method chooses the threshold by minimizing what?

A The variance within pixel groups

B The total number of white pixels

C The image brightness

D The number of edges detected

Otsu's method finds the threshold that minimizes variance within foreground and background pixel groups.

Which method is best for images with shadows and bright spots?

A Binary thresholding

B Adaptive thresholding

C Otsu's thresholding

D Color thresholding

Adaptive thresholding handles uneven lighting by adjusting thresholds locally.

What is a typical application of Otsu's thresholding?

A Noise reduction

B Color image segmentation

C Document text extraction

D Image sharpening

Otsu's method is commonly used to separate text from background in scanned documents.

Explain the differences between binary, adaptive, and Otsu's thresholding methods.

Think about how each method chooses the cutoff value.

Describe a real-life scenario where adaptive thresholding is better than binary thresholding.

Consider photos taken outdoors with sunlight and shadows.

Practice

1. What is the main purpose of image thresholding in computer vision?

easy

A. To convert an image into black and white for easier analysis

B. To increase the color depth of an image

C. To blur the image for noise reduction

D. To resize the image to smaller dimensions

Solution

1. Step 1: Understand image thresholding

   Image thresholding simplifies images by turning pixels into black or white based on a cutoff value.

2. Step 2: Identify the purpose

   This simplification helps in easier analysis like object detection or segmentation.

3. Final Answer:

   To convert an image into black and white for easier analysis -> Option A

4. Quick Check:

   Image thresholding = black and white conversion [OK]

Hint: Thresholding means black and white conversion [OK]

Common Mistakes:

• Confusing thresholding with image resizing
• Thinking thresholding increases color depth
• Mixing thresholding with blurring

2. Which of the following is the correct syntax to apply binary thresholding using OpenCV in Python?

easy

A. ret, thresh = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)

B. ret, thresh = cv2.adaptiveThreshold(image, 127, 255, cv2.THRESH_BINARY)

C. thresh = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)

D. ret, thresh = cv2.threshold(image, 255, 127, cv2.THRESH_BINARY)

Solution

1. Step 1: Recall OpenCV binary threshold syntax

   The function cv2.threshold returns two values: the threshold used and the thresholded image.

2. Step 2: Check parameter order and function call

   Correct call is cv2.threshold(image, threshold_value, max_value, threshold_type).

3. Final Answer:

   ret, thresh = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY) -> Option A

4. Quick Check:

   cv2.threshold returns two values [OK]

Hint: cv2.threshold returns two values: ret and image [OK]

Common Mistakes:

• Using adaptiveThreshold instead of threshold for binary
• Not unpacking two return values
• Swapping threshold and max values

3. Given the following code snippet, what will be the value of ret after applying Otsu's thresholding?

import cv2
image = cv2.imread('image.jpg', 0)
ret, thresh = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
print(ret)

medium

A. The fixed threshold value 0

B. Always 255

C. The optimal threshold value found by Otsu's method

D. The maximum pixel value in the image

Solution

1. Step 1: Understand Otsu's thresholding output

   When using cv2.THRESH_OTSU, the function ignores the input threshold (0 here) and calculates an optimal threshold automatically.

2. Step 2: Identify what ret holds

   The variable ret stores the threshold value found by Otsu's method, not the input or max pixel value.

3. Final Answer:

   The optimal threshold value found by Otsu's method -> Option C

4. Quick Check:

   Otsu returns optimal threshold in ret [OK]

Hint: Otsu's ret is the best threshold found [OK]

Common Mistakes:

• Assuming ret is always 0 or max pixel value
• Confusing input threshold with output
• Thinking ret is the thresholded image

4. Identify the error in this adaptive thresholding code snippet and select the correct fix:

import cv2
image = cv2.imread('image.jpg', 0)
thresh = cv2.adaptiveThreshold(image, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 6, 2)

medium

A. Image must be read in color mode, not grayscale

B. Max value should be 127 instead of 255

C. Use cv2.THRESH_OTSU instead of cv2.THRESH_BINARY

D. Block size must be an odd number greater than 1; change 6 to 7

Solution

1. Step 1: Check adaptiveThreshold parameters

   The block size parameter must be an odd number greater than 1 to define the neighborhood size.

2. Step 2: Identify the error in block size

   The block size is 6, which is even and will cause a runtime error. It must be changed to an odd number greater than 1, such as 7.

3. Final Answer:

   Block size must be an odd number greater than 1; change 6 to 7 -> Option D

4. Quick Check:

   Block size odd and >1 [OK]

Hint: Block size in adaptiveThreshold must be odd > 1 [OK]

Common Mistakes:

• Using even block size causing runtime error
• Confusing max value with threshold value
• Reading image in color instead of grayscale

5. You have an image with uneven lighting. Which thresholding method should you choose to get the best binary segmentation, and why?

hard

A. Binary thresholding with a fixed value, because it is simple and fast

B. Adaptive thresholding, because it calculates thresholds locally for different regions

C. Otsu's thresholding, because it finds a global optimal threshold automatically

D. No thresholding, just use the original image

Solution

1. Step 1: Understand the problem of uneven lighting

   Uneven lighting means different parts of the image have different brightness levels, making a single global threshold ineffective.

2. Step 2: Compare thresholding methods

   Binary thresholding uses one fixed value, which fails with uneven lighting. Otsu's method finds one global threshold, also insufficient. Adaptive thresholding calculates thresholds for small regions, handling uneven lighting well.

3. Final Answer:

   Adaptive thresholding, because it calculates thresholds locally for different regions -> Option B

4. Quick Check:

   Uneven lighting = adaptive thresholding best [OK]

Hint: Uneven light? Use adaptive thresholding [OK]

Common Mistakes:

• Choosing global threshold methods for uneven lighting
• Ignoring lighting variation in images
• Skipping thresholding and using raw image