Computer VIsion and Robotics IS 7th Sem Syllabus for VTU BE 2017 Scheme (Professional Elective-4)

Computer VIsion and Robotics detail syllabus for Information Science Engineering (IS), 2017 scheme is taken from VTU official website and presented for VTU students. The course code (17CS752), and for exam duration, Teaching Hr/week, Practical Hr/week, Total Marks, internal marks, theory marks, duration and credits do visit complete sem subjects post given below.

For all other is 7th sem syllabus for be 2017 scheme vtu you can visit IS 7th Sem syllabus for BE 2017 Scheme VTU Subjects. For all other Professional Elective-4 subjects do refer to Professional Elective-4. The detail syllabus for computer vision and robotics is as follows.

Module 1

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.

Module 2
For complete syllabus and results, class timetable and more pls download iStudy. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.

Module 3

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,

Module 4

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.

Module 5
For complete syllabus and results, class timetable and more pls download iStudy. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.

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:

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

For detail syllabus of all other subjects of BE Is, 2017 regulation do visit Is 7th Sem syllabus for 2017 Regulation.

Dont forget to download iStudy for latest syllabus and results, class timetable and more.