The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Oriented edge forests for boundary detection
Open accessAbstract
We present a simple, efficient model for learning boundary detection based on a random forest classifier. Our approach combines (1) efficient clustering of training examples based on a simple partitioning of the space of local edge orientations and (2) scale-dependent calibration of individual tree output probabilities prior to multiscale combination. The resulting model outperforms published results on the challenging BSDS500 boundary detection benchmark. Further, on large datasets our model requires substantially less memory for training and speeds up training time by a factor of 10 over the structured forest model.
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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
Representing and Recognizing the Visual Appearance of Materials Using Three-Dimensional Textons
Leung, T., & Malik, J. (2001) · International Journal of Computer Vision · doi:10.1023/a:1011126920638 · Cited by 1,498 · cited 1×
“These statistics can be captured in a variety of ways, e.g. via textons [15], sparse coding [22], or measures of self-similarity [14].”
BackgroundCrisp Boundary Detection Using Pointwise Mutual Information
Isola, P., Zoran, D., Krishnan, D., & Adelson, E. H. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10578-9_52 · Cited by 199
Cited by
Learning Deep Representation for Imbalanced Classification
Huang, C., Li, Y., Loy, C. C., & Tang, X. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.580 · Cited by 988
Holistically-nested edge detection
Xie, S., & Tu, Z. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-017-1004-z · Cited by 682
Bi-Directional Cascade Network for Perceptual Edge Detection
He, J., Zhang, S., Yang, M., Shan, Y., & Huang, T. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00395 · Cited by 361
Recent advances on image edge detection: A comprehensive review
Jing, J., Liu, S., Wang, G., Zhang, W., & Sun, C. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.06.083 · Cited by 298
Portable System for Box Volume Measurement Based on Line-Structured Light Vision and Deep Learning
Peng, T., Zhang, Z., Song, Y., Chen, F., & Zeng, D. (2019) · Sensors · doi:10.3390/s19183921 · Cited by 20
Metadata
- Authors
- Hallman, S., & Fowlkes, C. C.
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 1732-1740
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1929880813
- Cited by
- 177
- References
- 3
- FWCI
- 7.76
- Type
- conference paper
- Language
- en
- Open access
- green
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