Interactive digital photomontage
ACM SIGGRAPH 2004 Papers
Direct HDR capture of the sun and sky
AFRIGRAPH '04 Proceedings of the 3rd international conference on Computer graphics, virtual reality, visualisation and interaction in Africa
Geometric Context from a Single Image
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Learning Outdoor Color Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2007 papers
Scene completion using millions of photographs
ACM SIGGRAPH 2007 papers
Modeling the World from Internet Photo Collections
International Journal of Computer Vision
What Does the Sky Tell Us about the Camera?
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Self organizing natural scene image retrieval
Expert Systems with Applications: An International Journal
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When we look at images taken from outdoor scenes, much of the information perceived is due to the ligthing conditions. In these scenes, the solar beams interact with the atmosphere and create a global illumination that determines the way we perceive objets in the world. Lately, exploration of the sky like the main illuminance component has began to be explored in Computer Vision. Some of these studies could be classified like color-based algorithms while some others fall in the physics-based category. However most of them assume that the photometric and geometric camera parameters are constant, or at least, that they could be determined. This work presents a simple and effective method in order to find images with similar lighting conditions. This method is based on a Gaussian mixture model of sky pixels represented by a 3D histogram in the La *b * color space.