Edge detection (Canny) in Computer Vision - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main goal of the Canny edge detection algorithm?

The main goal of the Canny edge detection algorithm is to find the edges in an image accurately by detecting sharp changes in brightness while reducing noise and avoiding false edges.

intermediate

List the main steps of the Canny edge detection process.

1. Noise reduction using a Gaussian filter.
2. Finding the intensity gradient of the image.
3. Non-maximum suppression to thin edges.
4. Double threshold to identify strong and weak edges.
5. Edge tracking by hysteresis to finalize edges.

intermediate

Why does Canny edge detection use two thresholds instead of one?

Canny uses two thresholds to separate strong edges from weak edges. Strong edges are definitely edges, while weak edges are only considered edges if they connect to strong edges. This helps reduce noise and false detections.

beginner

What role does the Gaussian filter play in Canny edge detection?

The Gaussian filter smooths the image to reduce noise before detecting edges. This helps prevent false edges caused by small fluctuations in pixel values.

intermediate

Explain what non-maximum suppression does in the Canny algorithm.

Non-maximum suppression thins the edges by keeping only the pixels that are local maxima in the gradient direction. This means it removes pixels that are not the strongest edge points, making edges thinner and clearer.

What is the first step in the Canny edge detection algorithm?

A Perform edge tracking by hysteresis

B Find intensity gradient

C Apply double threshold

D Apply Gaussian filter to reduce noise

The first step is to smooth the image with a Gaussian filter to reduce noise before detecting edges.

Why does Canny edge detection use two thresholds?

A To detect colors

B To separate strong edges from weak edges

C To blur the image

D To speed up the algorithm

Two thresholds help distinguish strong edges from weak edges, improving edge detection accuracy.

What does non-maximum suppression do?

A Thins edges by keeping only local maxima

B Blurs the image

C Detects corners

D Removes all edges

Non-maximum suppression thins edges by keeping only the strongest pixels along the gradient direction.

Which filter is used in Canny to reduce noise?

A Gaussian filter

B Median filter

C Sobel filter

D Laplacian filter

The Gaussian filter smooths the image to reduce noise before edge detection.

What is the final step in the Canny edge detection process?

A Non-maximum suppression

B Double threshold

C Edge tracking by hysteresis

D Gradient calculation

Edge tracking by hysteresis finalizes edges by connecting weak edges to strong edges.

Describe the full process of the Canny edge detection algorithm in your own words.

Think about how the algorithm finds edges step by step, starting from smoothing the image.

Explain why using two thresholds improves edge detection compared to using just one.

Consider how weak edges might be noise or real edges depending on their connection.

Practice

1. What is the main purpose of the Canny edge detection algorithm in computer vision?

easy

A. To resize images without losing quality

B. To colorize black and white images

C. To blur an image for noise reduction

D. To find clear edges in an image by detecting boundaries

Solution

1. Step 1: Understand the goal of edge detection

   Edge detection aims to find where objects start and end by detecting sharp changes in brightness.

2. Step 2: Recognize Canny's role

   Canny edge detection specifically finds clear edges by using gradients and thresholds to highlight boundaries.

3. Final Answer:

   To find clear edges in an image by detecting boundaries -> Option D

4. Quick Check:

   Edge detection = finding boundaries [OK]

Hint: Edges show object borders clearly in images [OK]

Common Mistakes:

• Confusing edge detection with image coloring
• Thinking Canny blurs images
• Assuming it resizes images

2. Which of the following is the correct way to call the Canny edge detector function in OpenCV (Python)?

easy

A. cv2.Canny(image, threshold1, threshold2)

B. cv2.canny(image, threshold1, threshold2)

C. cv2.Canny(image, threshold2, threshold1)

D. cv2.Canny(image)

Solution

1. Step 1: Recall OpenCV function naming

   OpenCV functions are case-sensitive; the correct function is Canny with uppercase C.

2. Step 2: Check required parameters

   The function requires the image and two threshold values in order: low threshold first, then high threshold.

3. Final Answer:

   cv2.Canny(image, threshold1, threshold2) -> Option A

4. Quick Check:

   Correct function name and parameter order = A [OK]

Hint: Function names are case-sensitive; check parameter order [OK]

Common Mistakes:

• Using lowercase 'canny' instead of 'Canny'
• Swapping threshold1 and threshold2
• Omitting required threshold parameters

3. Given the following Python code snippet using OpenCV, what will be the shape of the output image after applying Canny edge detection?

import cv2
image = cv2.imread('photo.jpg')
edges = cv2.Canny(image, 100, 200)
print(edges.shape)

medium

A. (height, width)

B. (height, width, 3)

C. (width, height)

D. (height, width, 1)

Solution

1. Step 1: Understand input image shape

   Original image read by cv2.imread is usually (height, width, 3) for color images.

2. Step 2: Check output of cv2.Canny

   Canny outputs a single-channel (grayscale) edge map, so shape is (height, width) without color channels.

3. Final Answer:

   (height, width) -> Option A

4. Quick Check:

   Canny output is grayscale edges = (height, width) [OK]

Hint: Canny output is single-channel grayscale image [OK]

Common Mistakes:

• Assuming output keeps 3 color channels
• Confusing width and height order
• Expecting a 3D shape for edges

4. You run Canny edge detection with thresholds 50 and 150 but get too many noisy edges. Which fix below correctly reduces noise in the output?

medium

A. Use a color image instead of grayscale

B. Decrease both thresholds to lower values

C. Increase both thresholds to higher values

D. Remove Gaussian blur before Canny

Solution

1. Step 1: Understand threshold effect on noise

   Lower thresholds detect more edges including noise; higher thresholds reduce noise by ignoring weak edges.

2. Step 2: Choose correct adjustment

   Increasing thresholds filters out weak noisy edges, improving edge quality.

3. Final Answer:

   Increase both thresholds to higher values -> Option C

4. Quick Check:

   Higher thresholds reduce noise in edges [OK]

Hint: Higher thresholds filter out weak noisy edges [OK]

Common Mistakes:

• Lowering thresholds increases noise
• Using color images directly confuses Canny
• Skipping blur preprocessing increases noise

5. You want to detect edges on a noisy grayscale image using Canny. Which sequence of steps will best improve edge detection results?

hard

A. Apply median blur, then Canny with low thresholds, then erode edges

B. Apply Gaussian blur, then Canny with tuned thresholds, then dilate edges

C. Apply Canny directly with default thresholds, then convert to color

D. Resize image larger, then apply Canny with high thresholds, then invert edges

Solution

1. Step 1: Preprocess noisy image with Gaussian blur

   Gaussian blur smooths noise while preserving edges, improving Canny input.

2. Step 2: Apply Canny with tuned thresholds

   Adjust thresholds to balance edge detection and noise filtering.

3. Step 3: Use dilation to strengthen edges

   Dilation thickens edges, making them clearer for further processing.

4. Final Answer:

   Apply Gaussian blur, then Canny with tuned thresholds, then dilate edges -> Option B

5. Quick Check:

   Blur + tuned thresholds + dilation = best edge detection [OK]

Hint: Blur first, tune thresholds, then enhance edges [OK]

Common Mistakes:

• Using low thresholds increases noise
• Skipping blur causes noisy edges
• Converting to color after Canny is useless