Corner detection (Harris) in Computer Vision - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main goal of the Harris corner detection algorithm?

The Harris corner detection algorithm aims to find points in an image where the intensity changes significantly in multiple directions, identifying corners or interest points.

intermediate

What is the role of the matrix M in Harris corner detection?

Matrix M summarizes the gradient changes around a pixel by combining the squared gradients in x and y directions and their product. It helps measure how much the image intensity changes in different directions.

intermediate

Explain the Harris response function R and how it helps detect corners.

The Harris response R is calculated from matrix M using the formula R = det(M) - k * (trace(M))^2. A large positive R indicates a corner, a small or negative R indicates edges or flat regions.

intermediate

Why is the parameter k important in the Harris corner detection algorithm?

Parameter k balances sensitivity between edges and corners. Typical values are between 0.04 and 0.06. It controls how much the trace of M affects the response R, helping to reduce false corner detections.

beginner

What are the main steps to perform Harris corner detection on an image?

1. Compute image gradients in x and y directions. 2. Calculate products of gradients and smooth them. 3. Form matrix M for each pixel. 4. Compute Harris response R. 5. Threshold R to find corners. 6. Optionally apply non-maximum suppression to refine corners.

What does a large positive Harris response R indicate at a pixel?

A A corner

B A flat region

C An edge

D Noise

A large positive R means strong intensity changes in multiple directions, which is characteristic of a corner.

Which of the following is NOT part of the Harris corner detection process?

A Calculating image gradients

B Applying a Gaussian filter

C Computing the Harris response R

D Using k-means clustering

K-means clustering is unrelated to Harris corner detection.

What does the matrix M in Harris detection represent?

A Image brightness

B Color histogram

C Intensity changes around a pixel

D Edge orientation

Matrix M summarizes how image intensity changes around a pixel in x and y directions.

What is the typical range for the parameter k in Harris corner detection?

A 0.5 to 1.0

B 0.04 to 0.06

C 1 to 10

D 10 to 100

The parameter k is usually set between 0.04 and 0.06 to balance corner sensitivity.

Why do we apply non-maximum suppression after computing Harris response?

A To keep only the strongest corner points

B To blur the image

C To increase the number of corners

D To convert image to grayscale

Non-maximum suppression removes weaker responses near strong corners to keep only the best points.

Describe the Harris corner detection algorithm and its main components.

Think about how intensity changes are measured and combined to find corners.

Explain why corners are important features in computer vision and how Harris detection helps find them.

Consider how corners provide unique information in images.

Practice

1. What is the main goal of the Harris corner detection algorithm in computer vision?

easy

A. To detect straight lines in an image

B. To find points in an image where edges meet, called corners

C. To blur the image for noise reduction

D. To segment the image into different color regions

Solution

1. Step 1: Understand the purpose of Harris corner detection

   Harris corner detection is designed to find corners, which are points where two edges meet in an image.

2. Step 2: Compare with other options

   Blurring, line detection, and segmentation are different tasks not performed by Harris corner detection.

3. Final Answer:

   To find points in an image where edges meet, called corners -> Option B

4. Quick Check:

   Harris detects corners = C [OK]

Hint: Corners are where edges meet, Harris finds these points [OK]

Common Mistakes:

• Confusing corner detection with edge detection
• Thinking Harris blurs or segments images
• Mixing up line detection with corner detection

2. Which of the following is the correct formula for the Harris corner response R?

easy

A. R = det(M) + k * (trace(M))^2

B. R = trace(M) - k * det(M)

C. R = det(M) - k * (trace(M))^2

D. R = det(M) / trace(M)

Solution

1. Step 1: Recall the Harris corner response formula

   The Harris response is calculated as R = det(M) - k * (trace(M))^2, where M is the second moment matrix and k is a sensitivity factor.

2. Step 2: Verify other options

   Other formulas either add instead of subtract or mix det and trace incorrectly.

3. Final Answer:

   R = det(M) - k * (trace(M))^2 -> Option C

4. Quick Check:

   Harris R formula uses det minus k times trace squared [OK]

Hint: Remember: R = det minus k times trace squared [OK]

Common Mistakes:

• Adding instead of subtracting in the formula
• Confusing determinant with trace
• Using division instead of subtraction

3. Given the following Python code snippet using OpenCV, what will be the output type of corners?

import cv2
import numpy as np
img = cv2.imread('image.jpg', 0)
corners = cv2.cornerHarris(img, 2, 3, 0.04)
print(type(corners))

medium

A. <class 'float'>

B. <class 'list'>

C. <class 'int'>

D. <class 'numpy.ndarray'>

Solution

1. Step 1: Understand OpenCV cornerHarris output

   The function cv2.cornerHarris returns a numpy array representing the corner response for each pixel.

2. Step 2: Check the printed type

   Printing type(corners) will show <class 'numpy.ndarray'> because corners is a numpy array.

3. Final Answer:

   <class 'numpy.ndarray'> -> Option D

4. Quick Check:

   cornerHarris returns numpy array [OK]

Hint: cornerHarris returns a numpy array of responses [OK]

Common Mistakes:

• Assuming output is a list instead of numpy array
• Thinking output is a single number
• Confusing output type with image type

4. In the code below, why does the Harris corner detection not highlight any corners?

import cv2
import numpy as np
img = cv2.imread('image.jpg', 0)
corners = cv2.cornerHarris(img, 2, 3, 0.04)
corners = cv2.dilate(corners, None)
img[corners > 0.01 * corners.max()] = 255
cv2.imshow('Corners', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

medium

A. The dilation step is missing a kernel argument

B. The threshold 0.01 * corners.max() is too high, no pixels pass

C. The image is grayscale, so corners cannot be detected

D. The assignment img[corners > threshold] = 255 modifies the original image incorrectly

Solution

1. Step 1: Analyze the dilation step

   The line corners = cv2.dilate(corners, None) fails with a TypeError because cv2.dilate requires a kernel (e.g., np.ones((3,3), np.uint8)). None is invalid, causing the code to crash before imshow.

2. Step 2: Rule out other options

   Grayscale works fine (A wrong), 0.01 threshold is standard (B wrong), direct modification to 255 is common for grayscale marking (D ok).

3. Final Answer:

   The dilation step is missing a kernel argument -> Option A

4. Quick Check:

   cv2.dilate requires kernel [OK]

Hint: cv2.dilate needs kernel like np.ones((3,3)); None causes TypeError [OK]

Common Mistakes:

• Thinking grayscale images can't have corners
• Assuming threshold is too high
• Believing img modification is incorrect (common for grayscale)

5. You want to detect strong corners in a noisy image using Harris corner detection. Which combination of steps will best improve corner detection accuracy?

hard

A. Apply Gaussian blur before detection, use a moderate window size, and set a proper threshold to filter weak corners

B. Use raw noisy image, large window size, and low threshold for more corners

C. Apply Gaussian blur before detection, use a smaller window size, and increase threshold

D. Skip blurring, use smallest window size, and no threshold to detect all corners

Solution

1. Step 1: Understand noise impact and preprocessing

   Noise can cause false corners, so applying Gaussian blur smooths the image and reduces noise effects.

2. Step 2: Choose window size and threshold carefully

   A moderate window size balances detail and noise, and a proper threshold filters out weak corners, improving accuracy.

3. Final Answer:

   Apply Gaussian blur before detection, use a moderate window size, and set a proper threshold to filter weak corners -> Option A

4. Quick Check:

   Blur + moderate window + threshold = better corners [OK]

Hint: Blur first, then moderate window and threshold for best corners [OK]

Common Mistakes:

• Ignoring noise and skipping blur
• Using too small or too large window size
• Setting threshold too low or too high