CEC Semenster Undefined
Fundamentals of Embedded Computer Vision: Creating Machines That See
Eric Gregori -
2012
Introduction to Embedded Vision
In this course we introduce embedded vision - the incorporation of computer vision techniques into embedded systems. Via case studies, we explore the functionality that systems can gain with embedded vision and provide a taste of typical vision algorithms. We also discuss technology trends that are enabling embedded vision to be used in cost-, energy- and size-limited applications, and we highlight challenges that must be addressed in integrating embedded vision capabilities into systems.
Course Resources
Special Educational Materials
2012
Fundamentals of Image Sensors for Embedded Vision
Image sensors are the "eyes" of embedded vision systems, and their characteristics largely determine the capabilities of the systems on which they are built. In this session, we introduce the most common types of 2D and 3D sensors used in embedded vision applications and explore their strengths and weaknesses. We also highlight recent developments in sensor technology.
Course Resources
Special Educational Materials
2012
Processor Choices for Embedded Vision
Embedded vision applications typically make heavy demands on processors - not just in terms of processing performance, but also regarding memory, I/O, and real-time behavior. In this class, we explore the processor requirements of embedded vision applications in quantitative and qualitative terms. We then discuss the six main types of processor used in embedded vision applications, highlighting their key strengths and weaknesses and how they are evolving over time.
Course Resources
Special Educational Materials
2012
Introduction to Vision Algorithms and Some Free Tools
At the heart of embedded vision are algorithms. These include algorithms for improving captured images, identifying features of interest, inferring the presence of objects, and reasoning about objects and motion. In this class, we introduce some fundamental algorithms, such as motion and line detection. We explain how these algorithms work, and illustrate them with demos (which are available for download). We also introduce OpenCV, which is a free, open source vision software library.
Course Resources
Special Educational Materials
2012
More Algorithms and More on Using OpenCV
Here, we present more complex embedded vision algorithm examples, including face detection and object tracking. As in the preceding session, we explain how these algorithms work, through demonstrations built with OpenCV. We also illustrate a quick and easy way to set up your own vision algorithm development environment using OpenCV. Finally, we provide pointers to additional resources for learning about embedded vision.
Course Resources
Special Educational Materials
Instructor
Eric Gregori