Ground truth dataset and baseline evaluations for intrinsic image algorithms
Name
Grosse-2009-Ground truth dataset and baseline evaluations for intrinsic image algorithms.pdf
Size
4.67 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Grosse, Roger Baker
Johnson, Micah K.
Adelson, Edward H.
Freeman, William T.
Date Issued
May 2010
Journal
Proceedings of the 2009 IEEE 12th International Conference on Computer Vision
Publisher
Institute of Electrical and Electronics Engineers
Citation
Grosse, R. et al. “Ground truth dataset and baseline evaluations for intrinsic image algorithms.” Computer Vision, 2009 IEEE 12th International Conference on. 2009. 2335-2342. © Copyright 2009 IEEE
Version
Final published version
Abstract
The intrinsic image decomposition aims to retrieve “intrinsic” properties of an image, such as shading and reflectance. To make it possible to quantitatively compare different approaches to this problem in realistic settings, we present a ground-truth dataset of intrinsic image decompositions for a variety of real-world objects. For each object, we separate an image of it into three components: Lambertian shading, reflectance, and specularities. We use our dataset to quantitatively compare several existing algorithms; we hope that this dataset will serve as a means for evaluating future work on intrinsic images.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICCV.2009.5459428
