Minghui Tan
Holly Rushmeier
Carol O'Sullivan
Abstract
It is known that humans can be insensitive to large changes in illumination. For example, if an object of interest is extracted from one digital photograph and inserted into another, we do not always notice the differences in illumination between the object and its new background. This inability to spot illumination inconsistencies is often the key to success in digital “doctoring” operations. We present a set of experiments in which we explore the perception of illumination in outdoor scenes. Our results can be used to predict when and why inconsistencies go unnoticed. Applications of the knowledge gained from our studies include smarter digital “cut-and-paste” and digital “fake” detection tools, and image-based composite scene backgrounds for layout and previsualization.
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- Patrick Cavanagh. 2005. The artist as neuroscientist. Nature 434, 301--307.Google Scholar
Cross Ref
- Xiaowu Chen, Ke Wang, and Xin Jin. 2011. Single image based illumination estimation for lighting virtual object in real scene. In CAD Graphics 2011. 450--455. Google ScholarDigital Library
- Cathy Ennis, Christopher Peters, and Carol O'Sullivan. 2008. Perceptual evaluation of position and orientation context rules for pedestrian formations. In Proceedings of the 5th Symposium on Applied Perception in Graphics and Visualization (APGV'08). 75--82. Google ScholarDigital Library
- Cathy Ennis, Christopher Peters, and Carol O'Sullivan. 2011. Perceptual effects of scene context and viewpoint for virtual pedestrian crowds. ACM Transactions on Applied Perception (TAP) 8, 2 (Feb. 2011), Article 10, 22 pages. Google ScholarDigital Library
- J. Hamill, R. McDonnell, S. Dobbyn, and C. O'Sullivan. 2005. Perceptual evaluation of impostor representations for virtual humans and buildings. Computer Graphics Forum 24, 3, 623--633.Google ScholarCross Ref
- Timothy Hospedales and Sethu Vijayakumar. 2009. Multisensory oddity detection as Bayesian inference. PLoS ONE 4, 1 (01 2009), e4205.Google Scholar
- Kevin Karsch, Varsha Hedau, David Forsyth, and Derek Hoiem. 2011. Rendering synthetic objects into legacy photographs. ACM Transactions on Graphics (SIGGRAPH Asia'11) 30, 6 (Dec. 2011), 157:1--157:12. Google ScholarDigital Library
- Kevin Karsch, Kalyan Sunkavalli, Nathan Carr, Hailin Jin, Rafael Fonte, Michael Sittig, and David Forsyth. 2014. Automatic scene inference for 3D object compositing. ACM Transactions on Graphics (TOG) 33, 3 (May 2014), 32:1--32:15. Google ScholarDigital Library
- Jan J. Koenderink, Andrea J. van Doorn, Astrid M. L. Kappers, Susan F. Te Pas, and Sylvia C. Pont. 2003. Illumination direction from texture shading. JOSA A 20, 6, 987--995.Google ScholarCross Ref
- Jan J. Koenderink, Andrea J. van Doorn, and Sylvia C. Pont. 2004. Light direction from shad(ow)ed random Gaussian surfaces. Perception 33, 12, 1405--1420.Google ScholarCross Ref
- Jean-Francois Lalonde and Alexei A. Efros. 2007. Using color compatibility for assessing image realism. In IEEE International Conference on Computer Vision (ICCV'07). 1--8.Google Scholar
- Jean-François Lalonde, Alexei A. Efros, and Srinivasa G. Narasimhan. 2009. Webcam clip art: Appearance and illuminant transfer from time-lapse sequences. ACM Transactions on Graphics (SIGGRAPH Asia'09) 28, 5 (Dec. 2009), 131:1--131:10. Google ScholarDigital Library
- Jean-François Lalonde, Alexei A. Efros, and Srinivasa G. Narasimhan. 2012. Estimating the natural illumination conditions from a single outdoor image. International Journal of Computer Vision 98, 2, 123--145. Google ScholarDigital Library
- Jean-François Lalonde and Iain Matthews. 2014. Lighting estimation in outdoor image collections. In Proc. of the International Conference on 3-D Vision (3DV'14). 131--138. Google ScholarDigital Library
- Jorge Lopez-Moreno, Elena Garces, Sunil Hadap, Erik Reinhard, and Diego Gutierrez. 2013. Multiple light source estimation in a single image. Computer Graphics Forum 32, 8, 170--182.Google ScholarCross Ref
- Jorge Lopez-Moreno, Veronica Sundstedt, Francisco Sangorrin, and Diego Gutierrez. 2010. Measuring the perception of light inconsistencies. In Proceedings of the 7th Symposium on Applied Perception in Graphics and Visualization (APGV'10). ACM, New York, NY, 25--32. Google ScholarDigital Library
- Yuri Ostrovsky, Patrick Cavanagh, and Pawan Sinha. 2005. Perceiving illumination inconsistencies in scenes. Perception 34 (Nov. 2005), 1301--1314.Google Scholar
- Carol O'Sullivan and Cathy Ennis. 2011. Metropolis: Multisensory simulation of a populated city. In Proceedings of the 2011 3rd International Conference on Games and Virtual Worlds for Serious Applications (VS-GAMES'11). 1--7. Google ScholarDigital Library
- A. J. Preetham, Peter Shirley, and Brian Smits. 1999. A practical analytic model for daylight. In Proceedings of ACM SIGGRAPH 1999. 91--100. Google ScholarDigital Library
- Erik Reinhard, Michael Ashikhmin, Bruce Gooch, and Peter Shirley. 2001. Color transfer between images. IEEE Computer Graphics and Applications, special issue on Applied Perception 21, 5 (Sept. 2001), 34--41. Google ScholarDigital Library
- Su Xue, Aseem Agarwala, Julie Dorsey, and Holly Rushmeier. 2012. Understanding and improving the realism of image composites. ACM Transactions on Graphics (TOG) 31, 4, 84:1--84:10. Google ScholarDigital Library
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- The Perception of Lighting Inconsistencies in Composite Outdoor Scenes
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