The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Structured Forests for Fast Edge Detection
Abstract
Edge detection is a critical component of many vision systems, including object detectors and image segmentation algorithms. Patches of edges exhibit well-known forms of local structure, such as straight lines or T-junctions. In this paper we take advantage of the structure present in local image patches to learn both an accurate and computationally efficient edge detector. We formulate the problem of predicting local edge masks in a structured learning framework applied to random decision forests. Our novel approach to learning decision trees robustly maps the structured labels to a discrete space on which standard information gain measures may be evaluated. The result is an approach that obtains real time performance that is orders of magnitude faster than many competing state-of-the-art approaches, while also achieving state-of-the-art edge detection results on the BSDS500 Segmentation dataset and NYU Depth dataset. Finally, we show the potential of our approach as a general purpose edge detector by showing our learned edge models generalize well across datasets.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Learning to Localize Objects with Structured Output Regression
Blaschko, M. B., & Lampert, C. H. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_2 · Cited by 324
Cited by
Edge Boxes: Locating Object Proposals from Edges
Zitnick, C. L., & Dollár, P. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_26 · Cited by 2,656
Self-citationBDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
Yu, F., Chen, H., Wang, X., Xian, W., Chen, Y., Liu, F., Madhavan, V., & Darrell, T. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00271 · Cited by 2,641
Learning Rich Features from RGB-D Images for Object Detection and Segmentation
Gupta, S., Girshick, R., Arbeláez, P., & Malik, J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10584-0_23 · Cited by 1,528
Reading text in the wild with convolutional neural networks
Jaderberg, M., Simonyan, K., Vedaldi, A., & Zisserman, A. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-015-0823-z · Cited by 1,191
Multiscale Combinatorial Grouping
Arbelaez, P., Pont-Tuset, J., Barron, J., Marques, F., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.49 · Cited by 1,155
What Makes for Effective Detection Proposals?
Hosang, J., Benenson, R., Dollar, P., & Schiele, B. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2465908 · Cited by 727
(2018) · arXiv (Cornell University) · Cited by 679
Learning Complexity-Aware Cascades for Deep Pedestrian Detection
Cai, Z., Saberian, M., & Vasconcelos, N. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.384 · Cited by 326
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
Affordance detection of tool parts from geometric features
Myers, A., Teo, C. L., Fermuller, C., & Aloimonos, Y. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2015.7139369 · Cited by 273
Non-maximum Suppression for Object Detection by Passing Messages Between Windows
Rothe, R., Guillaumin, M., & Van Gool, L. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-16865-4_19 · Cited by 218
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
Yu, F., Chen, H., Wang, X., Xian, W., Chen, Y., Liu, F., Madhavan, V., & Darrell, T. (2018) · arXiv · doi:10.48550/arxiv.1805.04687 · Cited by 204
Crisp 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
TorontoCity: Seeing the World with a Million Eyes
Wang, S., Bai, M., Mattyus, G., Chu, H., Luo, W., Yang, B., Liang, J., Cheverie, J., Fidler, S., & Urtasun, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.327 · Cited by 186
Dvořák, P., & Menze, B. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-42016-5_6 · Cited by 119
Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography
Bernard, O., Bosch, J. G., Heyde, B., Alessandrini, M., Barbosa, D., Camarasu-Pop, S., Cervenansky, F., Valette, S., Mirea, O., Bernier, M., et al. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2503890 · Cited by 110
Learning visual groups from co-occurrences in space and time
Isola, P., Zoran, D., Krishnan, D., & Adelson, E. H. (2015) · arXiv · doi:10.48550/arxiv.1511.06811 · Cited by 90
Leveraging Mid-Level Semantic Boundary Cues for Automated Lymph Node Detection
Seff, A., Lu, L., Barbu, A., Roth, H., Shin, H.-C., & Summers, R. M. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-24571-3_7 · Cited by 36
Complete 3D Scene Parsing from an RGBD Image
Zou, C., Guo, R., Li, Z., & Hoiem, D. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-018-1133-z · Cited by 31
Semantic labeling for prosthetic vision
Horne, L., Alvarez, J., McCarthy, C., Salzmann, M., & Barnes, N. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.02.015 · Cited by 30
Optical Flow Estimation with Deep Learning, a Survey on Recent Advances
Savian, S., Elahi, M., & Tillo, T. (2020) · Unsupervised and Semi-Supervised Learning · doi:10.1007/978-3-030-32583-1_12 · Cited by 29
Metadata
- Authors
- Dollar, P., & Zitnick, C. L.
- Venue
- 2013 IEEE International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2013-12
- Pages
- 1841-1848
- OpenAlex
- W2129587342
- Cited by
- 899
- References
- 2
- FWCI
- 68.91
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
