The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
On the removal of shadows from images
Abstract
This paper is concerned with the derivation of a progression of shadow-free image representations. First, we show that adopting certain assumptions about lights and cameras leads to a 1D, gray-scale image representation which is illuminant invariant at each image pixel. We show that as a consequence, images represented in this form are shadow-free. We then extend this 1D representation to an equivalent 2D, chromaticity representation. We show that in this 2D representation, it is possible to relight all the image pixels in the same way, effectively deriving a 2D image representation which is additionally shadow-free. Finally, we show how to recover a 3D, full color shadow-free image representation by first (with the help of the 2D representation) identifying shadow edges. We then remove shadow edges from the edge-map of the original image by edge in-painting and we propose a method to reintegrate this thresholded edge map, thus deriving the sought-after 3D shadow-free image.
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Citations
Cites
A Computational Approach to Edge Detection
Canny, J. (1986) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1986.4767851 · Cited by 30,004
Swain, M. J., & Ballard, D. H. (1991) · International Journal of Computer Vision · doi:10.1007/bf00130487 · Cited by 5,623
Land, E. H., & McCann, J. J. (1971) · Journal of the Optical Society of America · doi:10.1364/josa.61.000001 · Cited by 4,095
(1986) · Cited by 3,616
Color Science, Concepts and Methods. Quantitative Data and Formulas
Wright, W. D. (1967) · Physics Bulletin · doi:10.1088/0031-9112/18/10/010 · Cited by 3,540
The Retinex Theory of Color Vision
Land, E. H. (1977) · Scientific American · doi:10.1038/scientificamerican1277-108 · Cited by 2,981
Stricker, M. A., & Orengo, M. (1995) · SPIE Proceedings · doi:10.1117/12.205308 · Cited by 1,747
A spatial processor model for object colour perception
Buchsbaum, G. (1980) · Journal of the Franklin Institute · doi:10.1016/0016-0032(80)90058-7 · Cited by 1,694
A method for enforcing integrability in shape from shading algorithms
Frankot, R. T., & Chellappa, R. (1988) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.3909 · Cited by 1,038
Mean shift analysis and applications
Comaniciu, D., & Meer, P. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790416 · Cited by 987
Cited by
Wang, J., Li, X., & Yang, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00192 · Cited by 465
Overview of Environment Perception for Intelligent Vehicles
Zhu, H., Yuen, K.-V., Mihaylova, L., & Leung, H. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2658662 · Cited by 386
Entropy Minimization for Shadow Removal
Finlayson, G. D., Drew, M. S., & Lu, C. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-009-0243-z · Cited by 349
Self-citationSingle-image shadow detection and removal using paired regions
Guo, R., Dai, Q., & Hoiem, D. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995725 · Cited by 295
Automatic Shadow Detection and Removal from a Single Image
Khan, S. H., Bennamoun, M., Sohel, F., & Togneri, R. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2462355 · Cited by 283
Paton, M., MacTavish, K., Ostafew, C. J., & Barfoot, T. D. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2015.7139391 · Cited by 30
Shadow Resistant Road Segmentation from a Mobile Monocular System
Álvarez, J. M., López, A. M., & Baldrich, R. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-72849-8_2 · Cited by 28
A hybrid vision-map method for urban road detection
Fernández, C., Fernández-Llorca, D., & Sotelo, M. A. (2017) · Journal of Advanced Transportation · doi:10.1155/2017/7090549 · Cited by 20
Metadata
- Authors
- Finlayson, G. D., Hordley, S. D., Lu, C., & Drew, M. S.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2006-01
- Volume
- 28
- Issue
- 1
- Pages
- 59-68
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2166502676
- Cited by
- 657
- References
- 25
- FWCI
- 27.44
- Type
- journal article
- Language
- en
- Open access
- closed
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