The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Learning low-level vision
Abstract
We show a learning-based method for low-level vision problems-estimating scenes from images. We generate a synthetic world of scenes and their corresponding rendered images. We model that world with a Markov network, learning the network parameters from the examples. Bayesian belief propagation allows us to efficiently find a local maximum of the posterior probability for the scene, given the image. We call this approach VISTA-Vision by Image/Scene TrAining. We apply VISTA to the "super-resolution" problem (estimating high frequency details from a low-resolution image), showing good results. For the motion estimation problem, we show figure/ground discrimination, solution of the aperture problem, and filling-in arising from application of the same probabilistic machinery.
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Cites
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
The Laplacian pyramid as a compact image code
Burt, P., & Adelson, E. (1983) · IEEE Transactions on Communications · doi:10.1109/tcom.1983.1095851 · Cited by 6,077
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694
Summation and Division by Neurons in Primate Visual Cortex
Carandini, M., & Heeger, D. J. (1994) · Science · doi:10.1126/science.8191289 · Cited by 649
Cited by
Super-resolution from a single image
Glasner, D., Bagon, S., & Irani, M. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459271 · Cited by 1,889
Semantic Image Segmentation via Deep Parsing Network
Liu, Z., Li, X., Luo, P., Loy, C.-C., & Tang, X. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.162 · Cited by 675
(2000) · doi:10.1109/afgr.2000.840616 · Cited by 634
Hallucinating Face by Eigentransformation
Wang, X., & Tang, X. (2005) · IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2005.848171 · Cited by 482
Fundamental limits of reconstruction-based superresolution algorithms under local translation
Lin, Z., & Shum, H.-Y. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.1261081 · Cited by 426
Metadata
- Authors
- Freeman, W. T., & Pasztor, E. C.
- Venue
- Proceedings of the Seventh IEEE International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 1999
- Pages
- 1182-1189 vol.2
- OpenAlex
- W2146914716
- Cited by
- 345
- References
- 4
- FWCI
- 8.20
- Type
- conference paper
- Language
- en
- Open access
- closed
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