Course detail

Computational Photography

FIT-VYFAcad. year: 2019/2020

Current digital cameras almost completely surpass traditional photography. They do not only capture light, they in fact compute
pictures. That said, there is practically no image that would not be
computationally processed to some extent today. Visual computing is
ubiquitous. Unfortunately, images taken by amateur photographers often
lack the qualities of professional photos and some image editing is
necessary. Computational photography (CP) develops methods to enhance or extend the capabilities of the current digital imaging chain.

Learning outcomes of the course unit

Not applicable.

Prerequisites

Not applicable.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Radke, R.: Computer Vision for Visual Effects. Cambridge university press.  2013.
Szeliski, R.: Computer Vision: Algorithms and Applications, Springer. 2010.
Shirley, P., Marschner, S.: Fundamentals of Computer Graphics. CRC Press. 2009.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

  1. Project proposals
  2. Project assignments
  3. Consultations after the lecture - literature
  4. Consultations after the lecture - implementation
  5. Consultations after the lecture - testing
  6. WRITTEN EXAM
  7. Finished implementations
  8. Presentations of assignments, final reports

Exam prerequisites:
It is obligatory to be present at the written exam, submit the project including textual report and oral presentation. At least 50 points must be obtained, while the minimal score from the test is 16 points, the minimal score from the project is 24 points. During the term, one can get bonus points in practical photography challenges.

Language of instruction

Czech

Work placements

Not applicable.

Aims

The aim is to introduce computational photography methods (http://cphoto.fit.vutbr.cz/) and to get acquainted with the principles of mathematics and computer science in the field.

Classification of course in study plans

  • Programme IT-MGR-2 Master's

    branch MBI , any year of study, summer semester, 5 credits, optional
    branch MPV , any year of study, summer semester, 5 credits, optional
    branch MGM , any year of study, summer semester, 5 credits, optional
    branch MSK , any year of study, summer semester, 5 credits, optional
    branch MIS , any year of study, summer semester, 5 credits, optional
    branch MBS , any year of study, summer semester, 5 credits, optional
    branch MIN , any year of study, summer semester, 5 credits, optional
    branch MMI , any year of study, summer semester, 5 credits, optional
    branch MMM , any year of study, summer semester, 5 credits, optional

  • Programme MITAI Master's

    specialization NADE , any year of study, summer semester, 5 credits, optional
    specialization NBIO , any year of study, summer semester, 5 credits, optional
    specialization NGRI , any year of study, summer semester, 5 credits, optional
    specialization NNET , any year of study, summer semester, 5 credits, optional
    specialization NVIZ , any year of study, summer semester, 5 credits, optional
    specialization NCPS , any year of study, summer semester, 5 credits, optional
    specialization NSEC , any year of study, summer semester, 5 credits, optional
    specialization NEMB , any year of study, summer semester, 5 credits, optional
    specialization NHPC , any year of study, summer semester, 5 credits, optional
    specialization NISD , any year of study, summer semester, 5 credits, optional
    specialization NIDE , any year of study, summer semester, 5 credits, optional
    specialization NISY , any year of study, summer semester, 5 credits, optional
    specialization NMAL , any year of study, summer semester, 5 credits, optional
    specialization NMAT , any year of study, summer semester, 5 credits, optional
    specialization NSEN , any year of study, summer semester, 5 credits, optional
    specialization NVER , any year of study, summer semester, 5 credits, optional
    specialization NSPE , any year of study, summer semester, 5 credits, optional

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Syllabus

  1. introduction to CP, light and color
  2. photography, optics, physics, sensors, noise
  3. visual perception, natural image statistics
  4. image blending
  5. Color, color spaces, color transfer, color-to-grayscale image conversions
  6. High dynamic range (HDR) imaging - acquisition, storage and display
  7. High dynamic range (HDR) imaging - tone mapping, inverse tone mapping
  8. Image registration for computational photography
  9. Computational illumination, dual photography, illumination changes
  10. Image and video quality metrics
  11. Omnidirectional camera, lightfields, synthetic aperture
  12. Non-photorealistic camera, computational aesthetics
  13. Computational video, GraphCuts, editing software, guests

Project

26 hours, compulsory

Teacher / Lecturer

eLearning