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Specialization Elective
Credit Hour : 3
Synopsis
The aim of the course is to introduce students to the fundamentals of image formation, methods, and techniques of computer vision and pattern recognition. Topics to be explored include the introduction to computer vision, fundamentals of image formation, camera imaging geometry, feature detection and matching, stereo, motion estimation and tracking, image classification, scene understanding, and deep learning with neural networks.
Students will develop basic methods for applications that include finding known models in images, depth recovery from stereo, camera calibration, image stabilization, automated alignment, tracking, boundary detection, and recognition.
Course Content
1. Introduction to Computer Vision and Basic Concepts of Image Formation
-Introduction and Goals of Computer Vision and Image Processing, Image Formation Concepts.
2. Fundamental Concepts of Image Formation
-Radiometry, Geometric Transformations, Geometric Camera Models.
-Camera Calibration, Image Formation in a Stereo Vision Setup, Image Reconstruction from a Series of Projections.
3. Image Processing Concepts
-Image Transforms.
-Image Enhancement.
-Image Filtering, Colour Image Processing, Image Segmentation
4. Image Descriptors and Features
-Texture Descriptors, Colour Features, Edges/Boundaries.
-Object Boundary and Shape Representations.
-Interest or Corner Point Detectors, Histogram of Oriented Gradients, Scale Invariant Feature Transform, Speeded up Robust Features, Saliency
5. Applications of Computer Vision
-Artificial Neural Network for Pattern Classification, Convolutional Neural Networks, Autoencoders.
-Gesture Recognition, Motion Estimation and Object Tracking, Programming Assignments.
References
Richard Szeliski, Computer Vision: Algorithms and Applications, 2nd Edition , Springer, 2022.
Ai Publishing, Computer Vision for Beginners : Theory and Applications Using Python, 2nd Edition , Ai Publishing LLC, 2021.
Prepared By
Puan Rusnida Romli
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