Feature matching between images in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Feature Matching

Which of the following best describes the purpose of feature matching between images in computer vision?

A To find corresponding points between two images to understand their spatial relationship

B To increase the resolution of an image by adding more pixels

C To convert a color image into grayscale for easier processing

D To segment an image into different objects based on color

💡 Hint

Think about why we want to find points that look similar in two different pictures.

Feature matching helps identify points that correspond between images, which is essential for tasks like stitching images or 3D reconstruction.

❓ Predict Output

intermediate

Output of Feature Matching Code

What is the output of the following Python code snippet using OpenCV for feature matching?

Computer Vision

import cv2
img1 = cv2.imread('image1.jpg', 0)
img2 = cv2.imread('image2.jpg', 0)
sift = cv2.SIFT_create()
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)
bf = cv2.BFMatcher()
matches = bf.knnMatch(des1, des2, k=2)
good = []
for m, n in matches:
    if m.distance < 0.75 * n.distance:
        good.append(m)
print(len(good))

A A boolean indicating if the images are identical

B A list of keypoints detected in the first image

C A tuple containing descriptors of both images

D An integer representing the number of good matches found between the two images

💡 Hint

Look at what is printed at the end of the code.

The code prints the length of the list 'good', which contains matches that passed the ratio test, so it outputs the number of good matches.

❓ Model Choice

advanced

Choosing the Best Feature Detector

You want to match features between two images taken under different lighting conditions and slight rotations. Which feature detector is most suitable?

A Harris Corner Detector

B SIFT (Scale-Invariant Feature Transform)

C Canny Edge Detector

D Histogram of Oriented Gradients (HOG)

💡 Hint

Consider which detector is robust to scale, rotation, and lighting changes.

SIFT is designed to be invariant to scale and rotation and robust to lighting changes, making it ideal for matching features under such conditions.

❓ Metrics

advanced

Evaluating Feature Matching Quality

Which metric is commonly used to evaluate the quality of feature matching between two images?

A Number of inlier matches after applying RANSAC

B Mean squared error between pixel intensities

C Confusion matrix of classification labels

D Cross-entropy loss of a neural network

💡 Hint

Think about how to measure how many matches are geometrically consistent.

The number of inlier matches after RANSAC filtering indicates how many matches agree with a geometric model, reflecting match quality.

🔧 Debug

expert

Debugging Feature Matching Code

Consider this code snippet for feature matching. What error will it raise when run?

Computer Vision

import cv2
img1 = cv2.imread('img1.jpg', 0)
img2 = cv2.imread('img2.jpg', 0)
sift = cv2.SIFT_create()
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)
bf = cv2.BFMatcher()
matches = bf.match(des1, des2)
print(matches[0].queryIdx)

A IndexError because matches list is empty

B TypeError because BFMatcher.match expects descriptors as lists

C AttributeError because 'NoneType' object has no attribute 'shape'

D No error, prints the query index of the first match

💡 Hint

Check what happens if images are not loaded properly.

If the images are not found or loaded, img1 or img2 will be None, causing detectAndCompute to fail with an AttributeError.