A Computational Approach to Edge Detection
Abstract
This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.
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The phosphene encodings found by the model in the semantic boundary condition are comparable to those found with traditional preprocessing approaches and yield similar reconstruction quality compared to edge detection or holistic contour detection.
“The phosphene encodings that were found by the model in this condition are comparable to those found with traditional preprocessing approaches (see Figure 6e), and yield similar reconstruction quality (see Table 3) compared to edge detection (Canny, 1986) or holistic contour detection (Xie & Tu, 2017).”
From End-to-end optimization of prosthetic vision (2022) · cites this paper as Canny, 1986
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Al-Atabany, W. I., Tong, T., & Degenaar, P. A. (2010) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-9-52 · Cited by 25 · see citing works
The convergence of AI, IoT, and big data for advancing flood analytics research
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Multi-View Monocular Pose Estimation for Spacecraft Relative Navigation
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Quantification of α-Gal Antigen Removal in the Porcine Dermal Tissue by α-Galactosidase
Gao, H.-W., Li, S.-B., Sun, W. Q., Yun, Z.-M., Zhang, X., Song, J.-W., Zhang, S.-K., Leng, L., Ji, S.-P., Tan, Y.-X., et al. (2015) · Tissue Engineering Part C: Methods · doi:10.1089/ten.tec.2015.0129 · Cited by 23 · see citing works
Automatic Reconstruction of Semantic 3D Models from 2D Floor Plans
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Sketch2Code: Transformation of Sketches to UI in Real-time Using Deep Neural Network
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Metric-Driven Learning of Correspondence Weighting for 2-D/3-D Image Registration
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“Among these methods, there are the Canny [9,10] and Sobel [11] algorithms, which detect the edges in an image, and the various corner detection algorithms [12,13], which can provide good repeatability along with localization [14].”
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Small-body segmentation based on morphological features with a u-shaped network architecture
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End-to-end optimization of prosthetic vision
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“The phosphene encodings that were found by the model in this condition are comparable to those found with traditional preprocessing approaches (see Figure 6e), and yield similar reconstruction quality (see Table 3) compared to edge detection (Canny, 1986) or holistic contour detection (Xie & Tu, 2017).”
BackgroundMethodResultInfluential
- Authors
- Canny, J.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Canny, John
- ORCID
- Canny, John
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1986-11
- Volume
- PAMI-8
- Issue
- 6
- Pages
- 679-698
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2145023731
- Cited by
- 30,004
- References
- 27
- Citations by year
- 2026: 1097 · 2025: 1541 · 2024: 1432 · 2023: 1325 · 2022: 1327 · 2021: 1433 · 2020: 1316 · 2019: 1323 · 2018: 1252 · 2017: 1255 · 2016: 1301 · 2015: 1411 · 2014: 1359 · 2013: 1322 · 2012: 1306
- FWCI
- 56.37
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- Memoria digital de Canarias (Universidad de Las Palmas de Gran Canaria) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Object Detection Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- edge detection, surface steps, edge localization, uncertainty principle, scale space, feature synthesis, gradient magnitude, Gaussian smoothing, point spread function, signal-to-noise ratio
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