The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context
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Cites
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BackgroundSelf-citationTextons, the elements of texture perception, and their interactions
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“The term texton was coined by (Julesz 1981) for describing human textural perception, and is somewhat analogous to phonemes used in speech recognition.”
Background
Cited by
Microsoft COCO: Common Objects in Context
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Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
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Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
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Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
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Microsoft COCO: Common Objects in Context
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Conditional Random Fields as Recurrent Neural Networks
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“Works on this topic include: TextonBoost [28], TextonForest [27], and Random Forest-based human body pose estimation for Kinect [26].”
MethodENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
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Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation
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Pathology image analysis using segmentation deep learning algorithms
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Galleguillos, C., & Belongie, S. (2010) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2010.02.004 · Cited by 351
Surface Defect Detection via Entity Sparsity Pursuit With Intrinsic Priors
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SegSort: Segmentation by Discriminative Sorting of Segments
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ConvNet-Based Localization of Anatomical Structures in 3-D Medical Images
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Image Segmentation with Cascaded Hierarchical Models and Logistic Disjunctive Normal Networks
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SuRVoS: Super-Region Volume Segmentation workbench
Luengo, I., Darrow, M. C., Spink, M. C., Sun, Y., Dai, W., He, C. Y., Chiu, W., Pridmore, T., Ashton, A. W., Duke, E. M. H., et al. (2017) · Journal of Structural Biology · doi:10.1016/j.jsb.2017.02.007 · Cited by 89
Bayesian convolutional neural network based MRI brain extraction on nonhuman primates
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GeoF: Geodesic Forests for Learning Coupled Predictors
Kontschieder, P., Kohli, P., Shotton, J., & Criminisi, A. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.16 · Cited by 63
Self-citationSengupta, S., & Sturgess, P. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2015.7139442 · Cited by 54
Semantic labeling for prosthetic vision
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Metadata
- Authors
- Shotton, J., Winn, J., Rother, C., & Criminisi, A.
- Venue
- International Journal of Computer Vision
- Published
- 2007-12-01
- Volume
- 81
- Issue
- 1
- Pages
- 2-23
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2054279472
- Cited by
- 1,026
- References
- 5
- FWCI
- 30.55
- Type
- journal article
- Language
- en
- Open access
- closed
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
