Human Centered Computing Web Lecture Module

DESCRIPTION

Human Centered Computing has very many application areas in, e.g.,:

1. Human-Computer Interfaces and Human-Robot Interfaces
2. Social Media Analytics
3. Video Analysis
4. Affective Computing
5. Biometrics
6. Privacy Protection

This CVML Web Module focuses on Human Centered Computing theory, methodologies and its applications in the above-mentioned diverse domains towards facing new challenges ahead. The first lecture is an Introduction to Human Centered Computing. Face Detection is a very important task, typically the first in any Human Centered Computing application. Crowd Detection and Analysis is important in certain application areas, notably in surveillance. Face Recognition is an important topics in biometrics. Face Clustering is very useful both in biometrics and in media analytics. As Privacy Protection is a must in web applications and beyond, Face De-identification can cater such needs. Facial Feature Detection can be used both in Visual Speech Recognition and Facial Expression Recognition. Visual Speech Recognition can be used for lip reading and as an aid to speech recognition. Facial Expression Recognition is a prime topic in Affective Computing. Human Action Recognition and Gesture Recognition are essential in Human-Computer Interfaces and Human-Robot Interfaces.  Soccer Video Analysis and Athlete Motion Analysis are very useful in analyzing sports videos.

LECTURE LIST

1. Introduction to Human Centered Computing
2. Face Detection
3. Face and Object De-detection
4. Crowd Detection and Analysis
5. Face Recognition
6. Face Clustering
7. Face De-identification for Privacy Protection
8. Facial Feature Detection
9. Visual Speech Recognition
10. Facial Expression Recognition
11. Ηuman body posture and pose estimation
12. Human Action Recognition
13. Gesture Recognition
14. Athlete Motion Analysis
15. Soccer Video Analysis

CVML WEB LECTURE MODULE SCHEDULE

This module has been designed to be mastered within 1 month (or less), if you have proper background (at least early undergraduate student in an EE, ECE, CS, CSE or any Engineering or Exact Sciences Department).
We propose that you follow the above mentioned  Lecture order. You may want to skip few Lectures that might not be of immediate interest to you for later study.

On average you can study 4 lectures per week. The related effort is as follows:
Lecture pdf study and filling the related understanding questionnaire: 1-2 hours per lecture (on average, depending on your background).