Keywords
Artificial intelligenceComputer scienceComputer visionImage (mathematics)Probabilistic logicBayesian networkMarkov chainBayesian probabilityPattern recognition (psychology)Machine learning
Affiliated Institutions
Related Publications
Context and Hierarchy in a Probabilistic Image Model
It is widely conjectured that the excellent ROC performance of biological vision systems is due in large part to the exploitation of context at each of many levels in a part/who...
Image Super-Resolution Using Deep Convolutional Networks
We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is...
Object class recognition by unsupervised scale-invariant learning
We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible const...
Super-resolution from a single image
Methods for super-resolution can be broadly classified into two families of methods: (i) The classical multi-image super-resolution (combining images obtained at subpixel misali...
Publication Info
- Year
- 2000
- Type
- article
- Volume
- 40
- Issue
- 1
- Pages
- 25-47
- Citations
- 1466
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
1466
OpenAlex
Cite This
William T. Freeman
(2000).
Learning Low-Level Vision.
International Journal of Computer Vision
, 40
(1)
, 25-47.
https://doi.org/10.1023/a:1026501619075
Identifiers
- DOI
- 10.1023/a:1026501619075