Computer Vision
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Computer Vision Syllabus — Unit-wise Content
The official GTU curriculum for Computer Vision (subject code 3174206) as prescribed for CS-DS · Semester 7 divides the course into 8 units, covering 62 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (3174206)Unit 1 Overview of computer vision and its applications
- Computer Vision and Computer Graphics: What is Computer Vision - Low- level
- High-level Overview of Diverse Computer Vision Applications: Document Image Analysis
- Object Recognition
- Medical Image Analysis
- Content-Based Image Retrieval
- Video Data Processing
- Virtual Reality and Augmented Reality
Unit 2 Image processing
- Image Formation and Representation: Imaging geometry
- cameras and Projections
- rigid and affine transformation
- pixel transforms
- color transforms
- histogram processing
- histogram equalization
- Fourier transformation and its applications in sharpening
- blurring and noise removal
Unit 3 Feature detection
- Edge detection
- corner detection
- line and curve detection
- active contours, PCA, SIFT and HOG descriptors
- shape context descriptors
- Morphological operations
Unit 4 Shape representation and segmentation
- 8 20% Subject Code : 3174206 Subject Name : Computer Vision https://syllabus.gtu.ac.in/Page 2 Contour based representation
- Region based representation
- Deformable curves and surfaces
- Snakes and active contours
- Level set representations
- Fourier and wavelet descriptors
- Medial representations
- Multiresolution analysis
- Active contours
- split & merge
- region splitting
- region merging
- graph-based segmentation
- mean shift and model finding
- Normalized cut
Unit 5 Camera models and calibration
- Camera models
- intrinsic and extrinsic parameters
- radial lens distortion
- direct parameter calibration
- camera parameters from projection matrices
- weak perspective
- affine, and perspective camera models
Unit 6 Motion representation and estimation
- The motion field of rigid objects
- motion parallax
- optical flow
- the image brightness constancy equation
- affine flow
- differential techniques
- feature-based techniques
- regularization and robust estimation
Unit 7 Motion tracking and object recognition
- Statistical filtering
- iterated estimation
- observability and linear systems
- the Kalman filter
- appearance-based methods
- image Eigen spaces
Unit 8 Machine Learning for Computer Vision
- Introduction to Machine Learning
- Image Classification
- Object Detection
- Semantic Segmentation
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