Title:

Computational Photography

Code:VYF
Ac.Year:2016/2017
Term:Summer
Curriculums:
ProgrammeBranchYearDuty
IT-MSC-2MBI-Elective
IT-MSC-2MBS-Elective
IT-MSC-2MGM-Elective
IT-MSC-2MIN-Elective
IT-MSC-2MIS-Elective
IT-MSC-2MMI-Elective
IT-MSC-2MMM-Elective
IT-MSC-2MPV-Elective
IT-MSC-2MSK-Elective
Language:Czech
Credits:5
Completion:classified accreditation
Type of
instruction:
Hour/semLecturesSem. ExercisesLab. exercisesComp. exercisesOther
Hours:2600026
 ExaminationTestsExercisesLaboratoriesOther
Points:0400060
Guarantee:Čadík Martin, doc. Ing., Ph.D., DCGM
Lecturer:Čadík Martin, doc. Ing., Ph.D., DCGM
Faculty:Faculty of Information Technology BUT
Department:Department of Computer Graphics and Multimedia FIT BUT
Schedule:
DayLessonWeekRoomStartEndLect.Gr.St.G.EndG.
Tuelecture2017-05-02D020713:0014:501MITxxxx
Tuelecture2017-05-02D020713:0014:502MITxxxx
 
Learning objectives:
  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.
Description:
  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.
Syllabus of lectures:
 
  1. introduction to CP, light and color (slides, projects, final report template)
  2. photography, optics, physics, sensors, noise (slides)
  3. visual perception, natural image statistics (slides)
  4. image blending (slides)
  5. Color, color spaces, color transfer, color-to-grayscale image conversions (slides)
  6. High dynamic range (HDR) imaging - acquisition, storage and display (slides, HDR file)
  7. High dynamic range (HDR) imaging - tone mapping, inverse tone mapping (slides)
  8. Image registration for computational photography (slides, spherical /360x180/ panorama)
  9. Computational illumination, dual photography, illumination changes (slides)
  10. Image and video quality metrics (slides)
  11. Omnidirectional camera, lightfields, synthetic aperture (slides)
  12. Non-photorealistic camera, computational aesthetics (slides)
  13. Computational video, GraphCuts, presentations of projects
Fundamental literature:
 
  • Shirley, P., Marschner, S.: Fundamentals of Computer Graphics. CRC Press. 2009.
  • Szeliski, R.: Computer Vision: Algorithms and Applications, Springer. 2010.
  • Bradski, G. and Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library, O'Reilly. 2008.
Links:
 http://cadik.posvete.cz/
http://cphoto.fit.vutbr.cz/
Progress assessment:
  
  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:
  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.