Template matching in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Template Matching Basics

What is the main purpose of template matching in computer vision?

A To find the location of a smaller image (template) inside a larger image

B To classify images into different categories based on content

C To reduce the noise in an image by smoothing

D To segment an image into multiple regions based on color

💡 Hint

Think about matching a small picture inside a bigger one.

Template matching is used to locate a smaller image within a larger one by sliding the template over the image and comparing regions.

❓ Predict Output

intermediate

Output of Template Matching Result

Given the following Python code using OpenCV, what is the shape of the result matrix?

Computer Vision

import cv2
import numpy as np
img = np.zeros((100, 100), dtype=np.uint8)
template = np.zeros((20, 20), dtype=np.uint8)
result = cv2.matchTemplate(img, template, cv2.TM_CCOEFF_NORMED)
print(result.shape)

A (81, 81)

B (100, 100)

C (120, 120)

D (20, 20)

💡 Hint

The result size is the input image size minus the template size plus one.

The result matrix shape is (image_height - template_height + 1, image_width - template_width + 1). Here, 100 - 20 + 1 = 81.

❓ Model Choice

advanced

Choosing the Correct Matching Method

Which OpenCV template matching method is best when you want to find the location with the highest similarity score?

A cv2.TM_SQDIFF

B cv2.TM_CCOEFF_NORMED

C cv2.TM_SQDIFF_NORMED

D cv2.TM_CCORR

💡 Hint

Look for the method that normalizes and uses correlation coefficient.

cv2.TM_CCOEFF_NORMED returns values between -1 and 1, where 1 means perfect match, so the highest value indicates best match.

❓ Metrics

advanced

Interpreting Template Matching Scores

When using cv2.TM_SQDIFF method, what does a lower score in the result matrix indicate?

A No correlation between template and image region

B A worse match between template and image region

C A better match between template and image region

D The template is larger than the image region

💡 Hint

Think about squared differences and what a small value means.

cv2.TM_SQDIFF calculates squared differences; lower values mean the template and image region are more similar.

🔧 Debug

expert

Debugging Template Matching Code

What error will this code produce when run, and why?

import cv2
import numpy as np
img = np.zeros((50, 50), dtype=np.uint8)
template = np.zeros((60, 60), dtype=np.uint8)
result = cv2.matchTemplate(img, template, cv2.TM_CCOEFF_NORMED)
print(result.shape)

A No error, prints (1, 1)

B IndexError: index out of range

C TypeError: unsupported operand type(s) for -: 'int' and 'str'

D cv2.error: (-215:Assertion failed) (image.cols >= templ.cols) && (image.rows >= templ.rows) in function 'matchTemplate'

💡 Hint

Check the sizes of the image and template.

The template cannot be larger than the image. OpenCV raises an assertion error if template size exceeds image size.