Goal of this Course

In the simplest terms, computer vision is the discipline of "teaching machines how to see." This field dates back more than forty years, but the recent explosive growth of digital imaging technology makes the problems of automated image interpretation more exciting and relevant than ever. There are two major themes in the computer vision literature: 3D geometry and recognition. The first theme is about using vision as a source of metric 3D information: given one or more images of a scene taken by a camera with known or unknown parameters, how can we go from 2D to 3D, and how much can we tell about the 3D structure of the environment pictured in those images? The second theme, by contrast, is all about vision as a source of semantic information: can we recognize the objects, people, or activities pictured in the images, and understand the structure and relationships of different scene components just as a human would? This course will strive to provide a unified perspective on the different aspects of computer vision, and give students the ability to understand vision literature and implement components that are fundamental to many modern vision systems.

Who is this class for:This course is an entry level course for Computer Vision. Some basic programming knowledge is assumed and the course requires learners to complete programming tasks in Matlab/C/C++/Python.

Requirements

• Language: Chinese, English
• Programming skill: Matlab, C/C++, Python, OpenCV
• Instrument: Desktop/Notebook. OS: Windows/Linux/Mac
• Reading report: Submitted by web page. Each report with at least 300 words
• Programming report: Submitted by web page. Each with a brief report at most 1000 words, but with many program's illustrations
• No plagiary for reports and programs.

Office Hour: Monday 14:30-15:30