What computer vision encompasses - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Core tasks in computer vision

Which of the following is NOT a common task in computer vision?

A Speech recognition

B Object detection

C Image classification

D Image segmentation

💡 Hint

Think about what computer vision deals with: images and videos, not sounds.

Speech recognition deals with audio data, not images or videos, so it is outside the scope of computer vision.

🧠 Conceptual

intermediate

Applications of computer vision

Which of these is a real-world application of computer vision?

A Translating languages in real-time

B Generating music automatically

C Predicting stock market trends

D Detecting faces in photos

💡 Hint

Computer vision helps computers understand images and videos.

Detecting faces in photos is a classic computer vision task where the system identifies and locates faces.

❓ Model Choice

advanced

Choosing a model for image classification

You want to build a system that recognizes different types of animals in photos. Which model type is best suited for this task?

A Recurrent Neural Network (RNN)

B K-Means Clustering

C Convolutional Neural Network (CNN)

D Linear Regression

💡 Hint

Think about which model type is designed to process images effectively.

CNNs are designed to analyze images by detecting patterns like edges and shapes, making them ideal for image classification.

❓ Metrics

advanced

Evaluating object detection models

Which metric is commonly used to evaluate the accuracy of object detection models?

A Mean Squared Error (MSE)

B Intersection over Union (IoU)

C BLEU Score

D Perplexity

💡 Hint

This metric measures how well the predicted bounding box overlaps with the true bounding box.

IoU calculates the overlap between predicted and actual bounding boxes, which is key for object detection accuracy.

🔧 Debug

expert

Debugging image preprocessing code

What error will this Python code raise when preprocessing images for a CNN?

import numpy as np
from PIL import Image

img = Image.open('cat.jpg')
img_array = np.array(img)
img_array = img_array / 255.0
img_array = img_array.reshape((224, 224, 3))

A ValueError due to incorrect reshape size

B TypeError because division by 255 is invalid

C FileNotFoundError because 'cat.jpg' does not exist

D No error, code runs successfully

💡 Hint

Check the original image size and the reshape dimensions.

The reshape size must match the total number of pixels. If the image is not 224x224, reshape will fail.

Practice

1. What is the main goal of computer vision?

easy

A. To help computers understand images and videos

B. To write programs faster

C. To improve internet speed

D. To create video games

Solution

1. Step 1: Understand the purpose of computer vision

   Computer vision is about making computers see and understand visual data like images and videos.

2. Step 2: Compare options with this purpose

   Only To help computers understand images and videos matches this goal; others are unrelated to computer vision.

3. Final Answer:

   To help computers understand images and videos -> Option A

4. Quick Check:

   Computer vision = understanding images/videos [OK]

Hint: Remember: computer vision means 'computer sees' [OK]

Common Mistakes:

• Confusing computer vision with programming speed
• Thinking it's about internet or games

2. Which of these is a common task in computer vision?

easy

A. Calculating taxes

B. Compiling code

C. Sending emails

D. Recognizing objects in images

Solution

1. Step 1: Identify tasks related to computer vision

   Computer vision tasks include recognizing objects, faces, and reading text from images or videos.

2. Step 2: Match options to these tasks

   Only Recognizing objects in images fits as it involves recognizing objects in images.

3. Final Answer:

   Recognizing objects in images -> Option D

4. Quick Check:

   Object recognition = computer vision task [OK]

Hint: Think about what computers 'see' in pictures [OK]

Common Mistakes:

• Choosing unrelated tasks like compiling or emailing
• Confusing computer vision with other computer tasks

3. Given this code snippet, what will it print?

import cv2
image = cv2.imread('cat.jpg')
print(type(image))

medium

A. <class 'numpy.ndarray'>

B. <class 'NoneType'>

C. <class 'str'>

D. Error: cv2 not found

Solution

1. Step 1: Understand cv2.imread output

   cv2.imread reads an image file and returns a numpy array representing the image pixels.

2. Step 2: Check the type printed

   Printing type(image) will show <class 'numpy.ndarray'> if the image loads correctly.

3. Final Answer:

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

4. Quick Check:

   cv2.imread returns numpy array [OK]

Hint: cv2.imread returns image as numpy array [OK]

Common Mistakes:

• Thinking it returns NoneType if file exists
• Confusing with string type
• Assuming cv2 is missing

4. This code tries to detect faces. What is wrong?

import cv2
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface.xml')
image = cv2.imread('people.jpg')
faces = face_cascade.detectMultiScale(image)
print(len(faces))

medium

A. The cascade file name is incorrect or missing

B. cv2.imread should be cv2.readImage

C. detectMultiScale needs a grayscale image

D. print(len(faces)) should be print(faces.length)

Solution

1. Step 1: Check input type for detectMultiScale

   detectMultiScale requires a grayscale image, but the code passes a color image.

2. Step 2: Identify the fix

   Convert image to grayscale using cv2.cvtColor before detection.

3. Final Answer:

   detectMultiScale needs a grayscale image -> Option C

4. Quick Check:

   Face detection needs grayscale input [OK]

Hint: Face detection works on grayscale images only [OK]

Common Mistakes:

• Wrong cascade filename
• Using wrong cv2 function name
• Incorrect print syntax

5. You want to build a system that reads text from photos of street signs. Which computer vision task should you use?

hard

A. Image classification

B. Optical character recognition (OCR)

C. Object detection

D. Image segmentation

Solution

1. Step 1: Understand the task requirement

   Reading text from images means extracting characters and words from pictures.

2. Step 2: Match task to computer vision methods

   OCR is the process of recognizing text in images, perfect for reading street signs.

3. Final Answer:

   Optical character recognition (OCR) -> Option B

4. Quick Check:

   Text reading = OCR task [OK]

Hint: Text in images? Use OCR technology [OK]

Common Mistakes:

• Choosing object detection for text
• Confusing classification with text reading
• Using segmentation which separates regions