54 Must-Know Computer Vision Interview Questions and Answers 2024

Basic Computer Vision Concepts

1.
What is computer vision and how does it relate to human vision?
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2.
Describe the key components of a computer vision system.
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3.
Explain the concept of image segmentation in computer vision.
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4.
What is the difference between image processing and computer vision?
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5.
How does edge detection work in image analysis?
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6.
Discuss the role of convolutional neural networks (CNNs) in computer vision.
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7.
What’s the significance of depth perception in computer vision applications?
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8.
Explain the challenges of object recognition in varied lighting and orientations.
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Image Manipulation and Processing

9.
What are the common image preprocessing steps in a computer vision pipeline?
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10.
How does image resizing affect model performance?
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11.
What are some techniques to reduce noise in an image?
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12.
Explain how image augmentation can improve the performance of a vision model.
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13.
Discuss the concept of color spaces and their importance in image processing.
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Feature Detection and Extraction

14.
What are feature descriptors, and why are they important in computer vision?
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15.
Explain the Scale-Invariant Feature Transform (SIFT) algorithm.
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16.
Describe how the Histogram of Oriented Gradients (HOG) descriptor works.
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17.
Compare and contrast different image feature extraction methods.
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18.
What are Haar Cascades, and how are they used for object detection?
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Deep Learning in Computer Vision

19.
How do CNNs differ from traditional neural networks in terms of architecture?
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20.
Explain the purpose of pooling layers in a CNN.
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21.
What is transfer learning, and when would you apply it in computer vision?
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22.
Discuss the concept and advantages of using pre-trained models in computer vision.
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23.
What’s the difference between object detection and image classification?
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Object Detection and Recognition

24.
What algorithms can you use for real-time object detection?
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25.
Explain the YOLO (You Only Look Once) approach to object detection.
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26.
Discuss the Region-based CNN (R-CNN) family of algorithms for object detection.
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27.
How do image recognition models deal with occlusion?
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28.
Compare the use of one-stage vs. two-stage detectors for object detection.
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Semantic Segmentation and Scene Understanding

29.
What is the difference between semantic segmentation and instance segmentation?
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30.
Explain the Fully Convolutional Network (FCN) and its role in semantic segmentation.
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31.
Discuss applications of scene understanding in computer vision.
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32.
How can depth information be utilized in semantic segmentation?
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Pose Estimation and Motion Analysis

33.
What is pose estimation, and what are its applications?
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34.
How does optical flow contribute to understanding motion in videos?
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35.
Explain how CNNs can be used for human activity recognition in video data.
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Coding Challenges

36.
Write a Python program to perform edge detection using the Canny filter.
Answer:
37.
Implement a simple image classifier using a pre-trained CNN with TensorFlow.
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38.
Create a function to automatically crop images centering on the main object.
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39.
Write code to segment an image into superpixels using SLIC (Simple Linear Iterative Clustering).
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40.
Implement basic facial landmark detection using Dlib or OpenCV.
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Advanced Topics and Research

41.
What are Generative Adversarial Networks (GANs) and their role in computer vision?
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42.
Discuss few-shot learning and its challenges in computer vision.
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43.
Explain the concept of zero-shot learning in the context of image recognition.
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44.
How can reinforcement learning be applied to problems in computer vision?
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45.
What are Siamese networks and where are they applicable in computer vision?
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Model Training and Evaluation

46.
How do you handle overfitting in a computer vision model?
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47.
What are some common metrics to evaluate a computer vision system’s performance?
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48.
Discuss the importance of cross-validation in assessing a vision model.
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49.
Explain how the Intersection over Union (IoU) metric works for object detection models.
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Applications and Industry-Specific Questions

50.
How would you design a computer vision system for automatic license plate recognition?
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51.
Outline the computer vision technologies involved in autonomous vehicle navigation.
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52.
Propose an approach for medical image analysis using computer vision.
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53.
Discuss the use of computer vision in retail for product recognition and tracking.
Answer:
54.
How might augmented reality (AR) applications benefit from advances in computer vision?
Answer:

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54 Computer Vision interview questions

What is computer vision and how does it relate to human vision?

Describe the key components of a computer vision system.

Explain the concept of image segmentation in computer vision.

What is the difference between image processing and computer vision?

How does edge detection work in image analysis?

Discuss the role of convolutional neural networks (CNNs) in computer vision.

What’s the significance of depth perception in computer vision applications?

Explain the challenges of object recognition in varied lighting and orientations.

What are the common image preprocessing steps in a computer vision pipeline?

How does image resizing affect model performance?

What are some techniques to reduce noise in an image?

Explain how image augmentation can improve the performance of a vision model.

Discuss the concept of color spaces and their importance in image processing.

What are feature descriptors, and why are they important in computer vision?

Explain the Scale-Invariant Feature Transform (SIFT) algorithm.

Describe how the Histogram of Oriented Gradients (HOG) descriptor works.

Compare and contrast different image feature extraction methods.

What are Haar Cascades, and how are they used for object detection?

How do CNNs differ from traditional neural networks in terms of architecture?

Explain the purpose of pooling layers in a CNN.

What is transfer learning, and when would you apply it in computer vision?

Discuss the concept and advantages of using pre-trained models in computer vision.

What’s the difference between object detection and image classification?

What algorithms can you use for real-time object detection?

Explain the YOLO (You Only Look Once) approach to object detection.

Discuss the Region-based CNN (R-CNN) family of algorithms for object detection.

How do image recognition models deal with occlusion?

Compare the use of one-stage vs. two-stage detectors for object detection.

What is the difference between semantic segmentation and instance segmentation?

Explain the Fully Convolutional Network (FCN) and its role in semantic segmentation.

Discuss applications of scene understanding in computer vision.

How can depth information be utilized in semantic segmentation?

What is pose estimation, and what are its applications?

How does optical flow contribute to understanding motion in videos?

Explain how CNNs can be used for human activity recognition in video data.

Write a Python program to perform edge detection using the Canny filter.

Implement a simple image classifier using a pre-trained CNN with TensorFlow.

Create a function to automatically crop images centering on the main object.

Write code to segment an image into superpixels using SLIC (Simple Linear Iterative Clustering).

Implement basic facial landmark detection using Dlib or OpenCV.

What are Generative Adversarial Networks (GANs) and their role in computer vision?

Discuss few-shot learning and its challenges in computer vision.

Explain the concept of zero-shot learning in the context of image recognition.

How can reinforcement learning be applied to problems in computer vision?

What are Siamese networks and where are they applicable in computer vision?

How do you handle overfitting in a computer vision model?

What are some common metrics to evaluate a computer vision system’s performance?

Discuss the importance of cross-validation in assessing a vision model.

Explain how the Intersection over Union (IoU) metric works for object detection models.

How would you design a computer vision system for automatic license plate recognition?

Outline the computer vision technologies involved in autonomous vehicle navigation.

Propose an approach for medical image analysis using computer vision.

Discuss the use of computer vision in retail for product recognition and tracking.

How might augmented reality (AR) applications benefit from advances in computer vision?

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