CVG-001 IGNOU Guess Paper 2026-27

CVG-001 Guess Paper (Digital PDF): Term-End Exam (TEE) Focus

This structured guess paper for CVG-001: Computer Vision and Graphics (English/Hindi) synthesizes recurring question patterns from IGNOU’s CBCS curriculum, validated against 5+ years of solved TEE papers (June & December sessions). It emphasizes time management for a 3-hour exam, with a 60:30:10 weightage split for short-answer (10 marks), long-answer (20 marks), and case-study questions (5 marks).

Key Syllabus Units Covered

• Unit 1: Image Representation and Processing
  • Focus areas: Pixel-level operations, grayscale/color space conversions, and histogram equalization techniques (2019 Dec TEE repeat).
  • Common pitfalls: Confusing spatial filtering with frequency-domain filtering (e.g., Gaussian vs. Laplacian kernels).
• Unit 3: Geometric Transformations
  • High-weight topics: Homogeneous coordinate systems, affine transformations, and perspective projection matrices (2021 June TEE, 15 marks).
  • Practical application: Deriving transformation equations for 2D/3D objects (e.g., scaling + rotation composites).
• Unit 5: 3D Modeling and Rendering
  • Critical concepts: Ray tracing vs. rasterization, shading models (Phong/Blinn), and texture mapping (2020 Dec TEE, 25 marks).
  • FAQ: ‘How to differentiate between Gouraud and Phong shading in an exam?’ Answer: Gouraud interpolates vertex colors; Phong interpolates normals.
• Unit 7: Computer Animation
  • Recurring questions: Keyframe interpolation, motion blur techniques, and inverse kinematics (IK) basics (2018 June TEE, 10 marks).
  • FAQ: ‘What is the difference between forward and inverse kinematics?’ Answer: Forward kinematics computes end-effector position; inverse kinematics solves joint angles for a target position.
• Unit 9: Advanced Topics in Computer Vision
  • Focus: SIFT/SURF feature detection, Hough transform for line detection, and object recognition pipelines (2022 Dec TEE, 12 marks).
  • Exam tip: Always mention scale invariance and orientation invariance when describing SIFT.

Chapter-Wise Question Weightage (Approximate)

• Image Processing (Units 1–2): 30% (Short: 5Qs × 2M; Long: 1Q × 10M)
• Geometric/3D Transformations (Units 3–5): 40% (Long: 2Qs × 15M; Case-study: 1Q × 5M)
• Animation & Advanced CV (Units 7–9): 30% (Short: 3Qs × 2M; Long: 1Q × 10M)

3-Hour Exam Time Management Tips

• First 30 minutes: Skim all questions; allocate 10 minutes per short-answer (2 marks each).
• Long-answers (20 marks): Spend 25–30 minutes per question, prioritizing mathematical derivations (e.g., transformation matrices) over verbal descriptions.
• Case-studies (5 marks): Focus on step-by-step logic (e.g., ‘Explain how SIFT detects keypoints in a noisy image’).
• Avoid: Writing unnecessary code snippets unless explicitly asked (e.g., Python/OpenCV pseudocode).

Subject-Specific FAQs

• Q: How to handle questions on Morphological Operations?

  Answer with 3 steps: (1) Define dilation/erosion, (2) show kernel examples, (3) apply to a sample image (e.g., noise removal). Use structuring elements terminology.

• Q: What is the difference between homography and perspective transform?

  A homography is a 2D projective transform (3×3 matrix); a perspective transform is a 3D projection (homography + camera parameters). Always mention degree of freedom (8 for homography, 11 for perspective).

Note: This guess paper excludes theoretical questions on Neural Networks for CV (Unit 9) unless explicitly requested in recent TEE papers. Prioritize algorithmic and mathematical explanations.