The DOI matches its Crossref record.
Edge and Curve Detection for Visual Scene Analysis
Abstract
Simple sets of parallel operations are described which can be used to detect texture edges, "spots," and "streaks" in digitized pictures. It is shown that, by comparing the outputs of the operations corresponding to (e.g.,) edges of different sizes, one can construct a composite output in which edges between differently textured regions are detected, and isolated objects are also detected, but the objects composing the textures are ignored. Relationships between this class of picture processing operations and the Gestalt psychologists' laws of pictorial pattern organization are also discussed.
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Citations
Cited by
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
Soft-NMS — Improving Object Detection with One Line of Code
Bodla, N., Singh, B., Chellappa, R., & Davis, L. S. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.593 · Cited by 2,274
“It was first employed in edge detection techniques [25].”
BackgroundMethodTextural Features Corresponding to Visual Perception
Tamura, H., Mori, S., & Yamawaki, T. (1978) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1978.4309999 · Cited by 2,155
Multiscale vision transformers
Fan, H., Xiong, B., Mangalam, K., Li, Y., Yan, Z., Malik, J., & Feichtenhofer, C. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00675 · Cited by 1,407
“In a parallel development, the computer vision community developed multiscale processing, sometimes called “pyramid” strategies, with Rosenfeld and Thurston [85], Burt and Adelson [8], Koenderink [61], among the key papers.”
MethodWavelet transform domain filters: a spatially selective noise filtration technique
Xu, Y., Weaver, J. B., Healy, D. M., & Lu, J. (1994) · IEEE Transactions on Image Processing · doi:10.1109/83.336245 · Cited by 747
Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning
Chen, R. J., Chen, C., Li, Y., Chen, T. Y., Trister, A. D., Krishnan, R. G., & Mahmood, F. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01567 · Cited by 621
Bounding Box Regression With Uncertainty for Accurate Object Detection
He, Y., Zhu, C., Wang, J., Savvides, M., & Zhang, X. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00300 · Cited by 623
Adaptive NMS: Refining Pedestrian Detection in a Crowd
Liu, S., Huang, D., & Wang, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00662 · Cited by 413
“It is an essential component of many detection methods, such as edge detection [33], feature point detection [25] and object detection [32, 20, 21].”
Method
Metadata
- Authors
- Rosenfeld, A., & Thurston, M.
- Venue
- IEEE Transactions on Computers
- Published
- 1971-05
- Volume
- C-20
- Issue
- 5
- Pages
- 562-569
- ISSN
- 0018-9340 (ISSN-L)
- 1557-9956
- 2326-3814
- OpenAlex
- W1968245656
- Cited by
- 781
- References
- 0
- FWCI
- 18.79
- Type
- journal article
- Language
- en
- Open access
- closed
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