Corner detection (Harris) in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert the image to grayscale before processing.

Computer Vision

gray = cv2.[1](img, cv2.COLOR_BGR2GRAY)

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

AcvtColor

Bresize

CGaussianBlur

Dthreshold

Attempts:

3 left

💡 Hint

Common Mistakes

Using resize instead of color conversion

Using GaussianBlur instead of color conversion

We use cv2.cvtColor with cv2.COLOR_BGR2GRAY to convert the image to grayscale.

2fill in blank

medium

Complete the code to compute the Harris corner response.

Computer Vision

dst = cv2.cornerHarris(gray, [1], 3, 0.04)

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

A1

B3

C5

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1 or 2 which is too small

Using 5 which is larger than typical

The block size parameter is usually set to 3 for Harris corner detection.

3fill in blank

hard

Fix the error in thresholding the Harris response to mark corners.

Computer Vision

img[dst > [1] * dst.max()] = [0, 0, 255]

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

A0.01

B0.001

C0.1

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1 which selects no corners

Using too small values like 0.001 which selects too many points

Using 0.1 as the threshold factor helps select strong corners by comparing to the max response.

4fill in blank

hard

Fill both blanks to normalize and convert the Harris response for visualization.

Computer Vision

dst_norm = np.empty(dst.shape, dtype=np.float32)
cv2.normalize(dst, dst_norm, [1], [2], cv2.NORM_MINMAX)

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

A0

B255

C1

D-1

Attempts:

3 left

💡 Hint

Common Mistakes

Using 255 as max which is for 8-bit images

Using negative values which are invalid here

Normalization scales values between 0 and 1 for easier visualization.

5fill in blank

hard

Fill all three blanks to create a mask of corners and mark them on the image.

Computer Vision

mask = dst_norm > [1]
img[mask] = [[2], [3], 0]

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

A0.1

B0

C255

D128

Attempts:

3 left

💡 Hint

Common Mistakes

Using 0 as threshold selects all points

Using wrong color values for marking

We threshold at 0.1, then mark corners with red color [255, 128, 0].

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