Unit 1 Overview of computer vision and its applications

7 lecture hours

• Image Formation and Representation: Imaging geometry
• cameras and Projections
• rigid and affine transformation

Unit 2 Image Processing

9 lecture hours

• Pixel transforms
• color transforms
• histogram processing
• histogram equalization
• Fourier transformation and its applications in sharpening
• blurring and noise removal

Unit 3 Feature detection

7 lecture hours

• edge detection
• corner detection
• line and curve detection
• active contours
• SIFT and HOG descriptors
• shape context descriptors
• Morphological operations

Unit 4 Segmentation

6 lecture hours

• Active contours
• split & merge
• region splitting
• region merging
• graph-based segmentation
• mean shift and model finding
• Normalized cut

Unit 5 Camera calibration

5 lecture hours

• 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

4 lecture hours

• 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

4 lecture hours

• statistical filtering
• iterated estimation
• observability and linear systems
• the Kalman filter

Unit 8 Object recognition and shape representation

• appearance-based methods
• image eigenspaces 4 Subject Code: 3171614 w.e.f