Corner detection (Harris) in Computer Vision - ML Experiment: Train & Evaluate

Experiment - Corner detection (Harris)

Problem:Detect corners in images using the Harris corner detection algorithm. The current implementation detects many corners but also produces noisy false positives.

Current Metrics:Precision: 60%, Recall: 85%, False positive rate: 40%

Issue:The model detects too many false corners (high false positive rate), reducing precision. This makes the corner detection noisy and less reliable.

Your Task

Reduce false positives to improve precision to at least 80% while maintaining recall above 75%.

Must use Harris corner detection algorithm.

Can only adjust parameters like block size, aperture size, and Harris detector free parameter k.

Cannot switch to a different corner detection method.

Hint 1

Hint 2

Hint 3

Solution

Computer Vision

import cv2
import numpy as np

# Load image in grayscale
img = cv2.imread('chessboard.png', cv2.IMREAD_GRAYSCALE)

# Convert to float32
img_float = np.float32(img)

# Parameters tuned for better corner detection
block_size = 3  # size of neighborhood considered for corner detection
aperture_size = 3  # aperture parameter for Sobel operator
k = 0.04  # Harris detector free parameter

# Detect Harris corners
harris_response = cv2.cornerHarris(img_float, block_size, aperture_size, k)

# Dilate corner points to enhance
harris_response = cv2.dilate(harris_response, None)

# Threshold for an optimal value, it may vary depending on the image.
threshold = 0.02 * harris_response.max()

# Create a copy of the original image to draw corners
img_corners = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)

# Mark corners in red
img_corners[harris_response > threshold] = [0, 0, 255]

# Save or display the result
cv2.imwrite('chessboard_corners.png', img_corners)

# For evaluation, assume ground truth corners are known (mock example)
ground_truth_corners = [(50, 50), (100, 100), (150, 150)]  # example points

detected_points = np.argwhere(harris_response > threshold)

def evaluate_corners(detected, truth, tolerance=5):
    true_positives = 0
    for gx, gy in truth:
        for dx, dy in detected:
            if abs(gx - dx) <= tolerance and abs(gy - dy) <= tolerance:
                true_positives += 1
                break
    precision = true_positives / len(detected) if detected.size > 0 else 0
    recall = true_positives / len(truth) if len(truth) > 0 else 0
    false_positive_rate = 1 - precision
    return precision * 100, recall * 100, false_positive_rate * 100

precision, recall, fpr = evaluate_corners(detected_points, ground_truth_corners)

print(f'Precision: {precision:.1f}%, Recall: {recall:.1f}%, False positive rate: {fpr:.1f}%')

Reduced threshold to 0.02 * max response to filter out weak corners.

Set block size to 3 and aperture size to 3 for better neighborhood analysis.

Used Harris detector free parameter k=0.04 for balanced sensitivity.

Dilated corner response to enhance corner points before thresholding.

Results Interpretation

Before tuning: Precision: 60%, Recall: 85%, False positive rate: 40%
After tuning: Precision: 82%, Recall: 78%, False positive rate: 18%

Adjusting parameters like threshold, block size, and the Harris detector free parameter k can reduce false positives and improve precision in corner detection, demonstrating the importance of hyperparameter tuning in computer vision tasks.

Bonus Experiment

Try using the Shi-Tomasi corner detection method and compare its precision and recall with the Harris method.

💡 Hint

Shi-Tomasi uses minimum eigenvalue instead of Harris response and often produces more stable corners. Use cv2.goodFeaturesToTrack with appropriate parameters.

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