Unit 1
Image Segmentation Algorithms: contextual, non-contextual segmentation, texture segmentation. Feature Detectors and Descriptors, Feature Matching-Object Recognition, Face detection (Viola Jones), Face Recognition.
| Course Name | Computer Vision |
| Course Code | 23AID301 |
| Program | B.Tech in Artificial Intelligence and Data Science |
| Semester | 5 |
| Credits | 3 |
| Campus | Coimbatore , Amritapuri ,Faridabad , Bangaluru, Amaravati |
Image Segmentation Algorithms: contextual, non-contextual segmentation, texture segmentation. Feature Detectors and Descriptors, Feature Matching-Object Recognition, Face detection (Viola Jones), Face Recognition.
Modern computer vision architectures based on deep convolutional neural networks, The Use of Motion in Segmentation Optical Flow & Tracking Algorithms, YOLO, DeepSORT: Deep Learning to Track Custom Objects in a Video, Action classification with convolutional neural networks, RNN, LSTM
Markov Random Fields (MRF), Decision Networks, From Bayesian Networks to Markov NetworksImage registration, 2D and 3D feature-based alignment, Pose estimation, Geometric intrinsic calibration, – Camera Models and Calibration: Camera Projection Models – Projective Geometry, transformation of 2-d and 3-d, Internal Parameters, Lens Distortion Models, Calibration Methods Geometry of Multiple views – Stereopsis, Camera and Epipolar Geometry, Fundamental matrix; Homography, Rectification, DLT, RANSAC, 3-D reconstruction framework; Auto-calibration., Introduction to SLAM (Simultaneous Localization and Mapping).
Course Objectives
.
Course Outcomes
After completing this course, students will be able to
|
CO1 |
To implement different image segmentation approaches |
|
CO2 |
To use different deep learning based object detection algorithms for real time applications |
|
CO3 |
To use various deep learning based object tracking algorithms on video data |
|
CO4 |
To implement 3D reconstruction algorithms for real time applications |
CO-PO Mapping
|
PO/PSO |
PO1 |
PO2 |
PO3 |
PO4 |
PO5 |
PO6 |
PO7 |
PO8 |
PO9 |
PO10 |
PO11 |
PO12 |
PSO1 |
PSO2 |
PSO3 |
|
CO |
|||||||||||||||
|
CO1 |
3 |
2 |
3 |
2 |
3 |
2 |
1 |
2 |
3 |
3 |
2 |
3 |
3 |
1 |
1 |
|
CO2 |
3 |
2 |
3 |
2 |
3 |
2 |
1 |
2 |
3 |
3 |
2 |
3 |
3 |
1 |
1 |
|
CO3 |
3 |
2 |
3 |
2 |
3 |
2 |
1 |
2 |
3 |
3 |
2 |
3 |
3 |
1 |
1 |
|
CO4 |
3 |
2 |
3 |
2 |
3 |
2 |
1 |
2 |
3 |
3 |
2 |
3 |
3 |
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
Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach, “Deep Learning (Adaptive Computation and Machine Learning series)”, January 2017, MIT Press.
Richard Szelinski, “Introduction to Computer Vision and its Application”, 2010.
Marco Treiber, “An Introduction to Object Recognition Selected Algorithms for a Wide Variety of Applications”, Springer, 2010.
Forsyth and Ponce, “Computer Vision: A Modern Approach”, Second Edition, Prentice Hall, 2011.
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