Course Purpose
This course introduces to the learner the fundamental concepts/issues of Computer Vision and Image Processing, and major approaches that address them.
Course Learning Outcomes
CLO 1: Explain the main building blocks of image processing and computer vision.
CLO 2: Enhance image features for better manipulation of the image.
CLO 3: Perform Preprocessing techniques to process and analyze an image.
CLO 4: Apply knowledge on computer vision to be able to describe an image
Course Content
Introduction to Computer Vision and Basic Concepts of Image Formation: Introduction and Goals of Computer Vision and Image Processing, Image Formation Concepts. Radiometry, Geometric Transformations, Geometric Camera Models, Camera Calibration, Image Formation in a Stereo Vision Setup, Image Reconstruction from a Series of Projections.
Image Processing: Image representation in color and light, Image Transforms, Image Enhancement, Image Filtering, Colour Image Processing, Image Segmentation, 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
Fundamentals of Machine Learning: Linear Regression, Basic Concepts of Decision Functions, Elementary Statistical Decision Theory, Parameter Estimation, Clustering for Knowledge Representation, Dimension Reduction, Linear Discriminant Analysis.
Computer Vision: Applications of Computer Vision: Artificial Neural Network for Pattern Classification, Convolutional Neural Networks, Autoencoders, Gesture Recognition, Motion Estimation and Object Tracking. Image Formation; Geometric camera models, Imaging with one camera, Multiple images; Stereopsis, Imaging with two cameras, Tracking.
Image Processing: Image representation in color and light, Image Transforms, Image Enhancement, Image Filtering, Colour Image Processing, Image Segmentation, 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
Fundamentals of Machine Learning: Linear Regression, Basic Concepts of Decision Functions, Elementary Statistical Decision Theory, Parameter Estimation, Clustering for Knowledge Representation, Dimension Reduction, Linear Discriminant Analysis.
Computer Vision: Applications of Computer Vision: Artificial Neural Network for Pattern Classification, Convolutional Neural Networks, Autoencoders, Gesture Recognition, Motion Estimation and Object Tracking. Image Formation; Geometric camera models, Imaging with one camera, Multiple images; Stereopsis, Imaging with two cameras, Tracking.