UCL DEPARTMENT OF COMPUTER SCIENCE Information for Students
	Home	Admissions	Students	Careers	Research	Business	People	Help

Text size A A A A A

| STUDENTS > Computational Photography and Capture |

Computational Photography and Capture

Note: Whilst every effort is made to keep the syllabus and assessment records correct for this course, the precise details must be checked with the lecturer(s).

Code:	3085 (Also taught as: GV15 and M085)
Year:	3
Prerequisites:	Completion of years 1 and 2 of the BSc/ BEng/ MEng Computer Science or CS with EE Programme
Term:	2
Taught By:	Tim Weyrich (50%) Gabriel Brostow (50%)
Aims:	The module is designed to be self-contained, introducing the theoretical and practical aspects of modern photography and capture algorithms to students with only limited mathematical background. The two primary aims are i) to introduce universal models of colour, computer-controlled cameras, lighting and shape capture, and ii) to motivate students to choose among the topics presented for either continuing study (for those considering MSc’s and PhD’s) or future careers in the fields of advanced imaging.
Learning Outcomes:	Students will develop in-depth knowledge and understanding of the main Computational Photography topics as listed in the attached outline syllabus.

Content:

Introduction to Computational Photography	More on cameras, sensors and colour Blending and compositing Background subtraction and matting Warping, morphing, mosaics and panoramas High-dynamic range imaging/ tome mapping Hybrid images Flash photography Stylised rendering using multi-flash
Image Inpainting	Texture synthesis Image quilting Heeger and Bergen Simplicial complex of morphable textures (Matusik 2005)
Extension to the temporal domain	TIP, Video textures Temporal sequence rendering Ezzat speech anim, comtrolled video sprites Video-based rendering: using photographs to enhance videos of a static scene Motion magnification Non-photorealistic rendering and animation
Colourization and colour transfer	colorization using optimization Colour transfer between images N-Dimensional probability density function transfer and its application to colour transfer
	Intrinsic images Vectorising Raster images Poisson image editing Seam carving
	De-blurring/ dehazing Coded aperture imaging
Image-based rendering	Image-based modelling and photo editing view dependence, light-dependence, plenoptic function Selected ways to capture the above representations
Extensions to the temporal domain	Factored time-lapse vidoe Computational time-lapse video Video synopsis and indexing
Capturing images with structured light	Laser-stripe projection ShadowCuts Stripe codes Edge codes Phase shift Brief recap of stereo, spatio-temporal stereo Photometric stereo The Helmholtz wheel (Helmholtz reciprocity)
Dual photography	seeing around corners dual light stage Separation of global and local reflectance Image-based BRDF measurements MEasuring the BSSRDF

Method of Instruction:

Lecture presentations supplemented by practical lab demonstration sessions and substantial online content with other detailed exaples and links for both further reading and existing demo software.

Assessment:

The course has the following assessment components:

Written Examination (2 hours, 70%)
Coursework Section (2 pieces, 30%)

To pass this course, students must:

Obtain an overall pass mark of 40% for all sections combined

The examination rubric is:
TBC

Resources:

Peter J Burt, Edward H Adelson. “The Laplacian pyramid as a compact image code,” [J]. IEEE Transaction on communications, 1983, 231: 532–540

Patrick Pérez, Michel Gangnet, Andrew Blake, “Poisson image editing”, Proceedings of ACM SIGGRAPH 2003, Pages: 313 – 318.

Pradeep Sen and Billy Chen and Gaurav Garg and Stephen R. Marschner and Mark Horowitz and Marc Levoy and Hendrik P. A. Lensch, “Dual photography” ACM Trans. Graphics, vol 24, num 3, p 745-755.

Tim Hawkins, Per Einarsson, Paul Debevec, “A Dual Light Stage”, Proceedings Eurographics Symposium on Rendering 2005

Youichi Horry, Ken-Ichi Anjyo, Kiyoshi Arai, “Tour into the picture: using a spidery mesh interface to make animation from a single image”, Proceedings of Siggraph 1997, Pages: 225 – 232.

Thaddeus Beier, Shawn Neely, “Feature-based image metamorphosis”, Proceedings of ACM SIGGRAPH 1992, pages 35-42.

Levin, Lischinski, Weiss, “Colorization using Optimization”, SIGGRAPH 2004.

Reinhard, Ashikhmin, Gooch, Shirley, “Color Transfer Between Images”, CGandA 2001.

Pritch, Rav-Acha, Peleg, “Video Synopsis and Indexing”, ICCV 2007.

Pravin Bhat and C. Lawrence Zitnick and Noah Snavely and Aseem Agarwala and Maneesh Agrawala and Brian Curless and Michael Cohen and Sing Bing Kang, “Using Photographs to Enhance Videos of a Static Scene”, Proceedings Eurographics Symposium on Rendering 2007, pp 327-338.

Arno Schödl, Richard Szeliski, David H. Salesin, and Irfan Essa, “Video Textures”, Proceedings of SIGGRAPH 2000, pages 489-498, July 2000.

Arno Schödl, Irfan Essa, “Controlled Animation of Video Sprites”, Symposium on Computer Animation 2002.

Pitié, Kokaram, Dahyot, “N-Dimensional Probability Density Function Transfer and its Application to Colour Transfer”, Proceedings of ICCV 2005, p.1434-1439.

Eric Bennett, Leonard McMillan, “Computational Time-Lapse Video”, ACM SIGGRAPH 2007, pp 102-108.

Sunkavalli, Matusik, Pfister, Rusinkiewicz, “Factored Time-Lapse Video”, Proceedings of Siggraph 2007.

Tony Ezzat, Gadi Geiger, Tomaso Poggio, “Trainable videorealistic speech animation”, Proceedings of Siggraph 2002, Pages: 388 – 398.

Land and McCann, “Lightness and Retinex Theory” J. Opt. Soc. Am. 61, 1-11 (1971).

H.G. Barrow and J.M. Tenenbaum, “Recovering Intrinsic Scene Characteristics From Images”, Computer Vision Systems, A. Hanson and E. Riseman, eds., pp. 3-26. Academic Press, 1978.

P. Sinha, E.H. Adelson, “Recovering Reflectance and Illumination in a World of Painted Polyhedra”, Proceedings of ICCV 1993, p. 156-163,

Yair Weiss, “Deriving Intrinsic Images from Image Sequences”, Proceedings ICCV 2001, pp. 68—75.

Marshall F. Tappen, William T. Freeman, Edward H. Adelson, “Recovering Intrinsic Images from a Single Image”, pp1343-1350.

Graham D. Finlayson, Mark S. Drew, and Cheng Lu, “Intrinsic Images by Entropy Minimization”, in Proceedings of ECCV pp. 582—595, 2004.

TBC

This page last modified: 26 May, 2010 by Nicola Alexander