CS-413 / 5 credits

Teacher: Süsstrunk Sabine

Language: English


Summary

The students will gain the theoretical knowledge in computational photography, which allows recording and processing a richer visual experience than traditional digital imaging. They will also execute practical group projects to develop their own computational photography application.

Content

Keywords

Computational Photography, Coded Image Sensing, Non-classical image capture, Multi-Image & Sensor Fusion, Mobile Imaging, Machine Learning

Learning Prerequisites

Recommended courses

  • Introduction to Computer Vision.
  • Signal Processing for Communications.
  • Machine Learning.

Important concepts to start the course

  • Basic signal/image processing.
  • Basic computer vision.
  • Basic programming (Python, iOS, Android).

Learning Outcomes

By the end of the course, the student must be able to:

  • Create a computational photography application.

In the programs

  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Computational photography
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Computational photography
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Computational photography
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Computational photography
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Computational photography
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Computational photography
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Computational photography
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Computational photography
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Computational photography
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Computational photography
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Computational photography
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Computational photography
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22