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CS601 Midterm Important Questions 2024

Data Communication PDF 83 KBUpdated 23 Oct 2026
Midterm past paper CS601 Midterm Important Questions 2024 PDF 83 KB
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Midterm past paper
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CS601 Data Communication
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PDF 83 KB
Updated
23 Oct 2026
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Cs601 · Midterm important questions 2024 · VU360

Here are some important questions from CS601 Virtual University Midterm 2024: Lecture 1-5:

Q1. What is the definition of computer vision and its importance?

Q2. How do you explain the concept of image processing and its techniques?

Q3. What are the different types of image processing techniques (filtering, transformation, etc.)?

Q4. How do you explain the concept of object recognition and its techniques?

Q5. What is the concept of image segmentation and its importance?

Lecture 6-10:

Q6. How do you explain the concept of feature extraction and its techniques?

Q7. What are the different types of feature extraction techniques (edge detection, thresholding, etc.)?

Q8. How do you explain the concept of object detection and its techniques?

Q9. What is the concept of image classification and its importance?

Q10. How do you explain the concept of deep learning in computer vision?

Lecture 11-15:

Q11. How do you explain the concept of convolutional neural networks (CNNs) in computer vision?

Q12. What are the different types of CNN architectures (AlexNet, VGG, etc.)?

Q13. How do you explain the concept of recurrent neural networks (RNNs) in computer vision?

Q14. What is the concept of transfer learning in computer vision?

Q15. How do you explain the concept of image generation and synthesis?

Lecture 16-20:

Q16. How do you explain the concept of image compression and its techniques?

Q17. What are the different types of image compression techniques (lossy, lossless, etc.)?

Q18. How do you explain the concept of image restoration and its techniques?

Q19. What is the concept of image quality assessment and its importance?

  1. How do you explain the concept of computer vision applications (healthcare, security, etc.)? Lecture 21-22:
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