Course detail
Computational Photography
FIT-VYFAcad. year: 2020/2021
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.
Supervisor
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
- Project proposals
- Project assignments
- Consultations after the lecture - literature
- Consultations after the lecture - implementation
- Consultations after the lecture - testing
- WRITTEN EXAM
- Finished implementations
- 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, elective
branch MPV , any year of study, summer semester, 5 credits, elective
branch MGM , any year of study, summer semester, 5 credits, elective
branch MSK , any year of study, summer semester, 5 credits, elective
branch MIS , any year of study, summer semester, 5 credits, elective
branch MBS , any year of study, summer semester, 5 credits, elective
branch MIN , any year of study, summer semester, 5 credits, elective
branch MMI , any year of study, summer semester, 5 credits, elective
branch MMM , any year of study, summer semester, 5 credits, elective - Programme MITAI Master's
specialization NADE , any year of study, summer semester, 5 credits, elective
specialization NBIO , any year of study, summer semester, 5 credits, elective
specialization NGRI , any year of study, summer semester, 5 credits, elective
specialization NNET , any year of study, summer semester, 5 credits, elective
specialization NVIZ , any year of study, summer semester, 5 credits, elective
specialization NCPS , any year of study, summer semester, 5 credits, elective
specialization NSEC , any year of study, summer semester, 5 credits, elective
specialization NEMB , any year of study, summer semester, 5 credits, elective
specialization NHPC , any year of study, summer semester, 5 credits, elective
specialization NISD , any year of study, summer semester, 5 credits, elective
specialization NIDE , any year of study, summer semester, 5 credits, elective
specialization NISY , any year of study, summer semester, 5 credits, elective
specialization NMAL , any year of study, summer semester, 5 credits, elective
specialization NMAT , any year of study, summer semester, 5 credits, elective
specialization NSEN , any year of study, summer semester, 5 credits, elective
specialization NVER , any year of study, summer semester, 5 credits, elective
specialization NSPE , any year of study, summer semester, 5 credits, elective
Type of course unit
Lecture
26 hours, optionally
Teacher / Lecturer
Syllabus
- introduction to CP, light and color
- photography, optics, physics, sensors, noise
- visual perception, natural image statistics
- image blending
- Color, color spaces, color transfer, color-to-grayscale image conversions
- High dynamic range (HDR) imaging - acquisition, storage and display
- High dynamic range (HDR) imaging - tone mapping, inverse tone mapping
- Image registration for computational photography
- Computational illumination, dual photography, illumination changes
- Image and video quality metrics
- Omnidirectional camera, lightfields, synthetic aperture
- Non-photorealistic camera, computational aesthetics
- Computational video, GraphCuts, editing software, guests