Unit 1
Introduction, Image Formation – geometric primitives and transformations, photometric image formation, digital camera, Camera calibration. Edge Detection, Segmentation.
| Course Name | Robotics Vision |
| Course Code | 23AID434 |
| Program | B.Tech in Artificial Intelligence and Data Science |
| Credits | 3 |
| Campus | Coimbatore , Amritapuri ,Faridabad , Bangaluru, Amaravati |
Introduction, Image Formation – geometric primitives and transformations, photometric image formation, digital camera, Camera calibration. Edge Detection, Segmentation.
Feature Detection and Matching – points and patches, edges, lines, Feature-Based Alignment – 2D, 3D feature-based alignment, pose estimation, Image Stitching, Dense motion estimation – Optical flow – layered motion, parametric motion, Structure from Motion.
Recognition – object detection, face recognition, instance recognition, category recognition, Stereo
Correspondence – Epipolar geometry, 3D reconstruction.
Course Objectives
Course Outcomes
After completing this course, students will be able to
|
CO1 |
Apply image formation and camera calibration for various applications. |
|
CO2 |
Analyze and select image features and apply for image matching. |
|
CO3 |
Develop image recognition algorithms |
|
CO4 |
Develop stereo vision applications for distance estimation. |
CO-PO Mapping
|
PO/PSO |
PO1 |
PO2 |
PO3 |
PO4 |
PO5 |
PO6 |
PO7 |
PO8 |
PO9 |
PO10 |
PO11 |
PO12 |
PSO1 |
PSO2 |
PSO3 |
|
CO |
|||||||||||||||
|
CO1 |
3 |
3 |
2 |
– |
2 |
2 |
1 |
1 |
1 |
– |
– |
– |
– |
– |
– |
|
CO2 |
3 |
3 |
2 |
3 |
3 |
3 |
2 |
1 |
2 |
1 |
– |
– |
– |
– |
– |
|
CO3 |
3 |
3 |
3 |
3 |
3 |
3 |
2 |
3 |
3 |
2 |
– |
– |
– |
– |
– |
|
CO4 |
3 |
3 |
1 |
2 |
3 |
2 |
1 |
1 |
1 |
1 |
– |
– |
– |
– |
– |
Evaluation Pattern
|
Assessment |
Internal/External |
Weightage (%) |
|
Assignments (minimum 2) |
Internal |
30 |
|
Quizzes (minimum 2) |
Internal |
20 |
|
Mid-Term Examination |
Internal |
20 |
|
Term Project/ End Semester Examination |
External |
30 |
Text Books / References
Szeliski R. Computer Vision: Algorithms and Applications Springer. New York. 2010..
Shapiro LG, Stockman GC. Computer Vision: Theory and Applications. 2001.
Forsyth DA, Ponce J. Computer Vision: a modern approach;2012.
Davies ER. Machine vision: theory, algorithms, practicalities. Elsevier; 2004 Dec 22.
Jain R, Kasturi R, Schunck BG. Machine vision. New York: McGraw-Hill; 1995 Mar 1
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