Displaying 273 resources
Digital Image Compression
This lecture overviews Digital Image Restoration that has many applications in scientific/medical imaging and in digital photography.
Camera Geometry
After a brief introduction to image acquisition and light reflection, the building blocks of modern cameras will be surveyed, along with geometric camera modelling.
Autonomous Underwater Vessels
This lecture overviews Autonomous Underwater Vessels that has many applications in ocean engineering. It covers the following topics in detail: AUV technologies, sensors, communications, applications.
Digital Image Processing
This lecture overviews Digital Image Processing that has many applications in improving image visual quality.
Immersion in Virtual Reality
This lecture overviews Immersion in Virtual Reality that has many applications in virtual presence and immersion. It covers the following topics in detail: Basic Concepts and Definitions.
Natural Language Processing
This lecture overviews Natural Language Processing (NLP) that has many applications in text analytics, Linguistics, Machine translation and sentiment analysis.
NLP and Text Sentiment Analysis
This lecture overviews Natural Language Processing (NLP) and Text Sentiment Analysis that has many applications in Text Analytics, Opinion extraction, Opinion mining, Sentiment mining, Subjectivity analysis.
Drone Mission Simulations
This lecture overviews various drone mission simulator architectures, notably AirSim and Gazebo.
Human Visual System
This lecture overviews the Human Visual System that has many applications in understanding image perception and image quality issues. It covers the following topics in detail: Human Visual System (eyes and visual pathway, stereo vision).
Agent Systems
This lecture overviews Agent Systems that has many applications in multi-party behavior modeling.
Autonomous Surface Vessels
Autonomous marine vessels can be described as surface (boats, ships) and underwater ones (submarines).
Introduction to Machine Learning
This lecture will cover the basic concepts of Machine Learning to alleviate inconsistencies towards concept and notation accuracy. Supervised, self-supervised, unsupervised, semi-supervised learning. Multi-task Machine Learning.