Shape from shading
Recovering high dynamic range radiance maps from photographs
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Photographic tone reproduction for digital images
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
A lighting reproduction approach to live-action compositing
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Image-based reconstruction of spatial appearance and geometric detail
ACM Transactions on Graphics (TOG)
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Multiple-cue Illumination Estimation in Textured Scenes
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Lightness perception inspired tone mapping
APGV '04 Proceedings of the 1st Symposium on Applied perception in graphics and visualization
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
CSIFT: A SIFT Descriptor with Color Invariant Characteristics
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Light source estimation using segmented HDR images
ACM SIGGRAPH 2007 posters
Dense 3D point cloud generation from multiple high-resolution spherical images
VAST'11 Proceedings of the 12th International conference on Virtual Reality, Archaeology and Cultural Heritage
Feature point detection under extreme lighting conditions
Proceedings of the 28th Spring Conference on Computer Graphics
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High Dynamic Range Images provide a more detailed information and their use in Computer Vision tasks is therefore desirable. However, the illumination distribution over the image often makes this kind of images difficult to use with common vision algorithms. In particular, the highlights and shadow parts in a HDR image are difficult to analyze in a standard way. In this paper, we propose a method to solve this problem by applying a preliminary step where we precisely compute the illumination distribution in the image. Having access to the illumination distribution allows us to subtract the highlights and shadows from the original image, yielding a material color image. This material color image can be used as input for standard computer vision algorithms, like the SIFT point matching algorithm and its variants.