17CS752 Computer Vision and Robotics syllabus for CS

CAMERAS:

Pinhole Cameras,

Radiometry – Measuring Light:

Light in Space, Light Surfaces, Important Special Cases,

Sources, Shadows, And Shading:

Qualitative Radiometry, Sources and Their Effects, Local Shading Models, Application: Photometric Stereo, Interreflections: Global Shading Models,

Color:

The Physics of Color, Human Color Perception, Representing Color, A Model for Image Color, Surface Color from Image Color.

Linear Filters:

Linear Filters and Convolution, Shift Invariant Linear Systems, Spatial Frequency and Fourier Transforms, Sampling and Aliasing, Filters as Templates,

Edge Detection:

Noise, Estimating Derivatives, Detecting Edges,

Texture:

Representing Texture, Analysis (and Synthesis) Using Oriented Pyramids, Application: Synthesis by Sampling Local Models, Shape from Texture.

The Geometry of Multiple Views:

Two Views,

Stereopsis:

Reconstruction, Human Stereposis, Binocular Fusion, Using More Cameras,

Segmentation by Clustering:

What Is Segmentation?, Human Vision: Grouping and Getstalt, Applications: Shot Boundary Detection and Background Subtraction, Image Segmentation by Clustering Pixels, Segmentation by Graph-Theoretic Clustering,

Segmentation by Fitting a Model:

The Hough Transform, Fitting Lines, Fitting Curves, Fitting as a Probabilistic Inference Problem, Robustness,

Segmentation and Fitting Using Probabilistic Methods:

Missing Data Problems, Fitting, and Segmentation, The EM Algorithm in Practice,

Tracking With Linear Dynamic Models:

Tracking as an Abstract Inference Problem, Linear Dynamic Models, Kalman Filtering, Data Association, Applications and Examples.

Geometric Camera Models:

Elements of Analytical Euclidean Geometry, Camera Parameters and the Perspective Projection, Affine Cameras and Affine Projection Equations,

Geometric Camera Calibration:

Least-Squares Parameter Estimation, A Linear Approach to Camera Calibration, Taking Radial Distortion into Account, Analytical Photogrammetry, An Application: Mobile Robot Localization,

Model- Based Vision:

Initial Assumptions, Obtaining Hypotheses by Pose Consistency, Obtaining Hypotheses by pose Clustering, Obtaining Hypotheses Using Invariants, Verification, Application: Registration In Medical Imaging Systems, Curved Surfaces and Alignment.

Course outcomes:

The students should be able to:

• Implement fundamental image processing techniques required for computer vision
• Perform shape analysis · Implement boundary tracking techniques
• Apply chain codes and other region descriptors
• Apply Hough Transform for line, circle, and ellipse detections.
• Apply 3D vision techniques.
• Implement motion related techniques.
• Develop applications using computer vision techniques.

Question paper pattern:

• The question paper will have ten questions.
• There will be 2 questions from each module.
• Each question will have questions covering all the topics under a module.
• The students will have to answer 5 full questions, selecting one full question from each module.

Text Books:

1. David A. Forsyth and Jean Ponce: Computer Vision – A Modern Approach, PHI Learning (Indian Edition), 2009.

Reference Books:

2. E. R. Davies: Computer and Machine Vision – Theory, Algorithms and Practicalities, Elsevier (Academic Press), 4th edition, 2013.