SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
IEEE Computer Graphics and Applications
ACM SIGGRAPH 2003 Papers
Exposing digital forgeries by detecting inconsistencies in lighting
MM&Sec '05 Proceedings of the 7th workshop on Multimedia and security
ACM SIGGRAPH 2006 Papers
ACM SIGGRAPH 2006 Papers
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Image and video matting: a survey
Foundations and Trends® in Computer Graphics and Vision
Multi-scale image harmonization
ACM SIGGRAPH 2010 papers
Progressive histogram reshaping for creative color transfer and tone reproduction
NPAR '10 Proceedings of the 8th International Symposium on Non-Photorealistic Animation and Rendering
Measuring the perception of light inconsistencies
Proceedings of the 7th Symposium on Applied Perception in Graphics and Visualization
Error-tolerant image compositing
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Local Laplacian filters: edge-aware image processing with a Laplacian pyramid
ACM SIGGRAPH 2011 papers
Learning photographic global tonal adjustment with a database of input/output image pairs
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Computational Color Constancy: Survey and Experiments
IEEE Transactions on Image Processing
Automatic exposure correction of consumer photographs
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Example-based video color grading
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
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Compositing is one of the most commonly performed operations in computer graphics. A realistic composite requires adjusting the appearance of the foreground and background so that they appear compatible; unfortunately, this task is challenging and poorly understood. We use statistical and visual perception experiments to study the realism of image composites. First, we evaluate a number of standard 2D image statistical measures, and identify those that are most significant in determining the realism of a composite. Then, we perform a human subjects experiment to determine how the changes in these key statistics influence human judgements of composite realism. Finally, we describe a data-driven algorithm that automatically adjusts these statistical measures in a foreground to make it more compatible with its background in a composite. We show a number of compositing results, and evaluate the performance of both our algorithm and previous work with a human subjects study.