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CS-803 (A) RGPV B.Tech

Image Processing and Computer Vision (IPCV)

Previous year papers and the official syllabus are free to read, no account needed.

Previous Year Papers

5 papers, latest Jun 2025

Free
See all 5 papers

Syllabus, notes and important questions for CS-803 (A)

Syllabus units

Topics as the official RGPV syllabus lists them.

Unit 1 Introduction to CVIP and Morphological Image Processing 24 topics
  • Basics of Computer Vision and Image Processing (CVIP)
  • History of CVIP
  • Evolution of CVIP
  • CV Models
  • Image Filtering
  • Image Representations
  • Image Statistics
  • Recognition Methodology – Conditioning
  • Recognition Methodology – Labeling
  • Recognition Methodology – Grouping
  • Recognition Methodology – Extracting
  • Recognition Methodology – Matching
  • Morphological Image Processing – Introduction
  • Morphological Image Processing – Dilation
  • Morphological Image Processing – Erosion
  • Morphological Image Processing – Opening
  • Morphological Image Processing – Closing
  • Morphological Image Processing – Hit‑or‑Miss transformation
  • Morphological operations on binary images
  • Morphological operations on gray‑scale images
  • Thinning
  • Thickening
  • Region growing
  • Region shrinking
Unit 1 notes
Unit 2 Image Representation, Description and Binary Vision 18 topics
  • Representation schemes
  • Boundary descriptors
  • Region descriptors
  • Binary Machine Vision – Thresholding
  • Binary Machine Vision – Segmentation
  • Binary Machine Vision – Connected component labeling
  • Binary Machine Vision – Hierarchical segmentation
  • Binary Machine Vision – Spatial clustering
  • Binary Machine Vision – Split & merge
  • Binary Machine Vision – Rule‑based segmentation
  • Binary Machine Vision – Motion‑based segmentation
  • Area Extraction – Concepts
  • Area Extraction – Data structures
  • Area Extraction – Edge detection
  • Area Extraction – Line linking
  • Area Extraction – Hough transform
  • Area Extraction – Line fitting
  • Area Extraction – Curve fitting (least‑square fitting)
Unit 2 notes
Unit 3 Region Analysis and Matching Frameworks 10 topics
  • Region properties
  • External points
  • Spatial moments
  • Mixed spatial gray‑level moments
  • Boundary analysis – Signature properties
  • Boundary analysis – Shape numbers
  • General frameworks for matching – Distance relational approach
  • General frameworks for matching – Ordered structural matching
  • General frameworks for matching – View class matching
  • Models database organization
Unit 3 notes
Unit 4 Facet Model Recognition and Projective Geometry 14 topics
  • Facet model recognition – Labeling lines
  • Understanding line drawings
  • Classification of shapes by edge labeling
  • Recognition of shapes
  • Consistent labeling problem
  • Back‑tracking algorithm
  • Perspective projective geometry
  • Inverse perspective projection
  • Photogrammetry – 2D to 3D reconstruction
  • Image matching – Intensity matching of ID signals
  • Image matching – 2D image matching
  • Image matching – Hierarchical image matching
  • Object models – 2D representation
  • Object models – Global vs. local features
Unit 4 notes
Unit 5 Knowledge‑Based Vision and Advanced Recognition 11 topics
  • Knowledge representation
  • Control strategies
  • Information integration
  • Object recognition – Hough transforms
  • Object recognition – Simple object recognition methods
  • Shape correspondence
  • Shape matching
  • Principal Component Analysis (PCA)
  • Feature extraction
  • Neural networks for image shape recognition
  • Machine learning for image shape recognition
Unit 5 notes
Read the full syllabus

Exam insights

Counted from 5 past CS-803 (A) papers.

Marks asked per paper, by unit

  • Unit 1 Introduction to computer vision and Image processing (CVIP) 36 marks
  • Unit 2 Image Representation and Description 22 marks
  • Unit 3 Region Analysis 10 marks
  • Unit 4 Facet Model Recognition 17 marks
  • Unit 5 Knowledge Based Vision 25 marks

Average marks of the questions from each unit in one paper. You answer only some of them, so the total is more than the paper’s maximum.

Asked again and again

  1. Explain how neural network structures are used for pattern / image shape recognition with an example.

    Asked 4 times: 2024, 2023, 2022

  2. Explain the backtracking algorithm and illustrate its application with an example such as consistency labelling or shape recognition.

    Asked 3 times: 2025, 2024, 2022

  3. Explain image segmentation and discuss thresholding and connected component labeling techniques.

    Asked 2 times: 2025, 2024

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