ELE 311: Computer Vision and Applications  (Graduate Course, First Semester)

OBJECTIVES: This course introduces the fundamentals of computer vision and its applications in several areas. Some emphasis is given to image based measurements and metrology, where the camera is the sensor. Techniques for handling the main problems in computer vision and their algorithms are discussed in detail. This is a graduate level course suited for graduate students in Computer Science, Engineering and Exact Sciences. It is primarily intended for highly motivated graduate students who are interested in doing research in the areas of Image Processing, Computer Vision and Image-Based Measurements. Many open problems in these areas suitable for investigation by Master's or Ph.D. students will be presented and proposed.

COURSE OUTLINE:

·         Introduction to Computer Vision and its Applications

·         Cameras: Models and Technologies

·         Radiometry: Light and Surfaces

·         Color, Shades and Shading

·         Image Representation: Attributes

·         Texture: Models and Representation

·         Scene Structure: 3D, Statistical Methods

·         Image Sequences: Motion Estimation and Detection

·         Image Based Measurements: Uncertainty and Metrology in Images and Videos

BIBLIOGRAPHY: 

·     Richard Szeliski, Computer Vision: Algorithms and Applications, Springer, 2010

Complementary

·   David Forsyth and Jean Ponce, Computer Vision: A Modern Approach, Prentice-Hall, 2002

·   ISO-GUM - Guide to the Expression of Uncertainty in Measurement

·   Selected papers (e.g.: IJCV, IEEE PAMI, CVPR, etc.)

Hours/Week: 4

Credits: 4